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Samarov, X. K., B. A. Tursunov, and Z. M. Tahirov. "DEVELOPMENT OF ALGORITHM SOFTWARE MODULE FOR ENHANCEMENT OF MESH NETWORK SECURITY." International Journal of Education, Social Science & Humanities. FARS Publishers 11, no. 3 (2023): 205–8. https://doi.org/10.5281/zenodo.7722267.
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In this work, the algorithm software module for improving the security of the mesh network, the mesh architecture and its implementation, which analysts associate with the next stage of growth in wireless networks, are considered. Challenges and obstacles for a global mesh network are outlined
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McDaid, Alexander, Eoghan Furey, and Kevin Curran. "Wireless Interference Analysis for Home IoT Security Vulnerability Detection." International Journal of Wireless Networks and Broadband Technologies 10, no. 2 (2021): 55–77. http://dx.doi.org/10.4018/ijwnbt.2021070104.
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The integrity of wireless networks that make up the clear majority of IoT networks lack the inherent security of their wired counterparts. With the growth of the internet of things (IoT) and its pervasive nature in the modern home environment, it has caused a spike in security concerns over how the network infrastructure handles, transmits, and stores data. New wireless attacks such as KeySniffer and other attacks of this type cannot be tracked by traditional solutions. Therefore, this study investigates if wireless spectrum frequency monitoring using interference analysis tools can aid in the monitoring of device signals within a home IoT network. This could be used enhance the security compliance guidelines set forth by OWASP and NIST for these network types and the devices associated. Active and passive network scanning tools are used to provide analysis of device vulnerability and as comparison for device discovery purposes. The work shows the advantages and disadvantages of this signal pattern testing technique compared to traditional network scanning methods. The authors demonstrate how RF spectrum analysis is an effective way of monitoring network traffic over the air waves but also possesses limitations in that knowledge is needed to decipher these patterns. This article demonstrates alternative methods of interference analysis detection.
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Mykhaylova, O., and T. Nakonechny. "SECURITY ANALYSIS OF MODERN WI-FI NETWORK PROTECTION PROTOCOLS: ASSESSMENT OF WPA3 PROTOCOL RESISTANCE DURING ATTACKS BASED ON DRAGONBLOOD UTILITY." Computer systems and network 6, no. 1 (2024): 133–47. http://dx.doi.org/10.23939/csn2024.01.133.
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With the constant development of information technology and the growing threat of cyber attacks the security of Wi-Fi wireless networks is of particular relevance. This article aims to provide an in-depth analysis of modern Wi-Fi security protocols such as WPA2 WPA3 and OWE focusing on their strengths and weaknesses in securing the network against the most common types of attacks. This paper looks at wireless network security threats including man-in-the-middle attacks Wi-Fi access point phishing and exploits that target specific security mechanisms. An important part of the research is a description of test methods attack tools such as Aircrack-ng and Wireshark and a detailed analysis of the results obtained. This work focuses on a detailed security analysis of the WPA3 protocol using Dragonblood utilities to identify possible vulnerabilities in its implementation. Through targeted attacks and simulations that mimic real-world cyberattacks the goal is to identify potential breaches of secure wireless networks using WPA3. This allows us to evaluate the effectiveness of the encryption and authentication mechanisms used within the framework of this standard and develop recommendations for increasing the level of security of information systems. During the study practical experiments will be conducted to modify the code of the wireless access point server and data obtained using the Wireshark program will be analyzed to assess the impact of attacks on the functionality of the network. The results of the work highlight the need for continuous improvements in Wi-Fi security technologies to provide reliable security in the face of growing cyber threats. Keywords of the article: Wireless networks Wi-Fi security protocols WPA2 WPA3 attacks on Wi-Fi methods of protecting against attacks information system security vulnerability analysis attack tools increasing the level of security user education network component software data encryption VPN.
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Ratnasari Ratnasari, Fernando Juliansyah, Gera Nugraha, Dede Wirta, and Didik Aribowo. "Quality of Service (QoS) Pada Jaringan Wi-Fi (Wireless Fidelity) FKIP UNTIRTA Menggunakan Software Wireshark." Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) 2, no. 2 (2024): 70–76. http://dx.doi.org/10.59061/jentik.v2i2.672.
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Wi-Fi (Wireless Fidelity) networks have become vital infrastructure in the era of modern wireless communications. With the rapid development of technology, monitoring and analyzing Wi-Fi networks has become increasingly important to ensure optimal performance and guaranteed security. Wireshark software has become one of the most popular and reliable tools in analyzing computer networks, including Wi-Fi networks. Wireshark is a very popular and powerful open source network analysis software. Originally known as Ethereum, Wireshark was developed by a team of volunteers and released under the GNU General Public License (GPL). The software can run on multiple platforms, including Windows, macOS, and Linux, making it a flexible tool for network analysis. The method used shows how to solve an analysis or analysis of a problem. This method section uses a literature study which is packaged briefly, clearly and concisely, so that it can be understood well. Another method used is to carry out simulation practices on Wireshark software to get results from a network. This abstract discusses the use of Wireshark in the context of Wi-Fi networks, covering basic Wi-Fi network concepts, monitoring and analysis methods, and security applications. Through careful monitoring and analysis with Wireshark, users can identify network performance issues, detect security threats, and optimize use of their Wi-Fi network. This abstract provides a comprehensive overview of Wireshark's role in understanding and effectively managing Wi-Fi networks.
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Kaliberda, Elena A., and Andrey M. Shabalin. "SOFTWARE AND HARDWARE STAND AS A MODERN TOOL FOR TEACHING STUDENTS TO CONFIGURE WIRELESS NETWORKS." Russian Journal of Education and Psychology 13, no. 6 (2022): 45–59. http://dx.doi.org/10.12731/2658-4034-2022-13-6-45-59.
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Goal. Theoretical justification and development of a software and hardware stand in order to train future information security specialists in setting up modern wireless networks.
 Work methodology or method. The study is based on scientific regulations of the practice-oriented learning organization. The competence-based approach was used as its methodological base, theoretical (comparison, analysis, generalization, concretization, modeling) methods were used. The development and implementation of a software and hardware stand in the educational process was carried out at Tyumen State University in 2021-2022 with the involvement of students from the “Computer Security” direction of training.
 Results. The authors have developed and applied in the educational process a software and hardware laboratory stand that combines virtualization tools for software and hardware components of a computer network.
 The stand is used to study the modern infrastructure of a computer network which is used in enterprises and companies by students of the “Computer Security” specialization. It can also be used while teaching modern wireless networks configuration for students of the directions: “Mathematical support and administration of information systems”, “Computer science and computer technology”, “Software engineering”.
 On the basis of the stand a cycle of laboratory works was developed for senior students of the “Computer security” specialization as part of the study of the “Wireless computer networks security” discipline.
 Scope of results. The results of the study can be applied in the field of training future IT-specialists in the higher education system.
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Mishbahuddin, Ahmad, Puspanda Hatta, and Cucuk Wawan Budiyanto. "ANALYSIS OF WIRELESS NETWORK SIMULATION BASED ON OPENWRT AND PFSENSE WITH QUALITY OF SERVICE INDICATORS ON LOW-COST NETWORK INFRASTRUCTURE." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1713–22. https://doi.org/10.52436/1.jutif.2024.5.6.4047.
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The need for high network traffic in Indonesia with the challenge of geographical topology that is difficult to reach makes the majority of internet network users access via wireless networks with limited budgets, resulting in poor internet QoS in Indonesia. An available solution to optimize low-cost network quality is to use the OpenWRT, open source router operating system with a focus on ease of application implementation in network projects. Another solution is pfSense, which is an open source router operation with a firewall network security focus to prevent intrusion. These two operating systems have different comparison methods in performance testing, making it difficult to make decisions about the performance differences between the two operating systems. This study aims to analyze the difference in performance and significance of open source operating systems with different development focuses on low-cost wireless network services. Analysis obtained from the quality of service measurement method on OpenWRT and Pfsense router operating systems on users that connected to a simulated wireless network topology. Data was collected by sending data between a number of users to the server and vice versa using iperf3 and mtr tools. The data consisted of QoS parameters: troughput, delay, jitter, and packet loss. The data shows that there are significant differences in several QoS parameters in the service of a number of users between the OpenWRT and PfSense operating systems. The results of this study show the limitations of each operating system in its implementation in low-cost wireless networks.
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Bolodurina, Irina P., Lyubov S. Grishina, Artur Yu Zhigalov, and Denis I. Parfenov. "Hybrid model of effective data representation in wireless transport networks with dynamic VANET topology." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 22, no. 1 (2022): 65–76. http://dx.doi.org/10.14529/ctcr220105.
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At the moment, traffic information systems require the aggregation of big data to provide recommendations to vehicles in the current conditions, which leads to an increase in user comfort. The main tool for improving the level of safety was timely informing traffic participants about the current situation on the road, weather conditions, etc. In this case, if the network object is subjected to an attack and the data is replaced during transmission, then the disclosure of confidential information, the creation of emergency si¬tuations, etc. is possible throughout the visibility zone of the VANET segment. In this regard, the most urgent issue is ensuring security, including when transmitting traffic, and conducting an additional analysis of big data about anomalies and ongoing unauthorized actions. Aim. To develop a hybrid model for the efficient placement of source and intermediate data in wireless transport networks with a dynamic VANET topology, which represents a structural representation of a software-configurable network and edge computing tools, with the ability to optimally analyze data from network nodes and identify anomalies. Methods. The considered Edge computing approach consists in locating computing capacities in geogra¬phically distributed computing devices closer to end users. Software-configurable SDN networks transfer part of the control and physical transmission functions from routing and switches, reducing the load. Within the framework of this study, an RD algorithm has been developed – a protocol for transmitting and processing intermediate data. To carry out clustering of vehicles on a network segment, the DBSCAN unsupervised learning method was used. Preliminary analysis of abnormal traffic was carried out on the basis of RNN neural network models with short-term memory. Results. The developed hybrid model of efficient placement of initial and intermediate data makes it possible to react faster to unauthorized actions. Conclusion. The results obtained in the course of the study confirm the need to implement and scale a hybrid model with boundary calculations in practice.
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Don, Africa, Aaron, and Torrizo, Lorwin Felimar. "Comparative Study of Intrusion Detection Systems against Mainstream Network Sniffing Tools." International Journal of Engineering & Technology 7, no. 4.16 (2018): 188–91. http://dx.doi.org/10.14419/ijet.v7i4.16.22884.
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As the world embraces a technological revolution on how everyday devices are connected to the internet, users provide sensitive information using the internet which is broken down and distributed as packets throughout the network. Packet sniffers tap to these packets, capable of potentially compromising security and privacy of unsuspecting users. This study aims to put into the test some well-known Intrusion Detection Systems (IDS) and observe how they fare against popular packet-sniffing tools such as Wireshark and tcpdump. The varied sniffing methods and techniques from various sniffing tools will provide an evaluation of performance of the intrusion detection systems.[1]  Ansari, S., Rajeev, S. & Chandrashekar, H. Packet Sniffing: A brief introduction. Potentials IEEE, Vol. 21 (5), (2002), pp. 17-19.[2]  Chomsiri, T. Sniffing packets on LAN without ARP spoofing.  IEEE in Convergence and Hybrid Information Technology ICCIT'08. Third International Conference, Vol. 2 (1), (2008), pp. 472-477.[3]  Anh, N. & Shorey, R. Network sniffing tools for WLANs: merits and limitations. 2005 IEEE International Conference on Personal Wireless Communications, (2005).[4]  Hu, Q., Asghar, M. & Brownlee, N. Evaluating network intrusion detection systems for high-speed networks. Telecommunication Networks and Applications Conference (ITNAC) 2017 27th International, (2017), pp. 1-6.[5]  Guo, K., Lu, H. & Yu, R. Packet Capture and Protocol Analysis Based on Winpcap. 2016 International Conference on Robots & Intelligent System (ICRIS), (2016).[6]  Goyal, P. & Goyal, A. Comparative study of two most popular packet sniffing tools-Tcpdump and Wireshark. 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), (2017).[7]  Meghana, J., Subashri, T. & Vimal, K. A survey on ARP cache poisoning and techniques for detection and mitigation. Signal Processing Communication and Networking (ICSCN). 2017 Fourth International Conference, (2017), pp. 1-6.[8]  Arzhakov, A. & Silnov, D. Architecture of multithreaded network scanner. IEEE Micro/Nanotechnologies and Electron Devices (EDM) 2017 18th International Conference of Young specialists, (2017), pp. 43-45.[9]  Bhosale, D. & Mane, V. Comparative study and analysis of network intrusion detection tools. 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), (2015).[10] Tirumala, S., Sathu, H. & Sarrafzadeh, A. Free and open source intrusion detection systems. Machine Learning and Cybernetics (ICMLC) 2015 International Conference, (2015).[11] Albin, E. & Rowe, N. A realistic experimental comparison of the suricata and snort intrusion -detection systems. Advanced Information Networking and Applications Workshops (WAINA) 26th International Conference, (2012).[12] Africa, A., Mesina, A., Izon, J. & Quitevis, B. Development of a Novel Android Controlled USB File Transfer Hub. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 9 (2-8), (2017), pp. 1-5.[13] SmartSniff. (2018). https://www.nirsoft.net/utils/smsniff.html.  [14] Wireshark. (2018).  https://www.wireshark.org/.[15] Windump. (2013). https://www.winpcap.org/windump/. [16] Dsniff, Dug Song. (2018). https://www.monkey.org/~dugsong/dsniff/.[17] Cain and Abel. (2018). http://www.oxid.it/cain.html. [18] Ettercap. (2018).  http://www.ettercap-project.org/ettercap/index.html. [19] Network grep. (2018).  http://ngrep.sourceforge.net/usage.html. [20] Nmap. (2018). https://nmap.org/. [21] Africa, A., Aguilar, J., Lim Jr., C., Pacheco, P. & Rodrin, S. Automated Aquaculture System that Regulates Ph, Temperature and Ammonia. 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2017).[22]S.Dhar. (2018). http://www.just.edu.jo/~tawalbeh/nyit/incs745/presentations/Sniffers.pdf.  [23] A. Africa, A Rough Set-Based Expert System for diagnosing information system communication networks. International Journal of Information and Communication Technology, Vol. 11 (4), (2017), pp. 496-512.[24]Africa, A., Bautista, S., Lardizabal, F., Patron, J. & Santos, A. Minimizing Passenger Congestion in Train Stations through Radio Frequency Identification (RFID) coupled with Database Monitoring System. ARPN Journal of Engineering and Applied Sciences, Vol. 12 (9), (2017), pp. 2863-2869.[25] Africa, A. & Cabatuan, M. A Rough Set Based Data Model for Breast Cancer Mammographic Mass Diagnostics. International Journal of Biomedical Engineering and Technology, Vol. 18 (4), (2015), pp. 359-369.[26] Africa, A. A Rough Set Based Solar Powered Flood Water Purification System with a Fuzzy Logic Model. ARPN Journal of Engineering and Applied Sciences, Vol. 12 (3), (2017), pp. 638-647.[27] Africa, A. A Mathematical Fuzzy Logic Control  Systems Model Using Rough Set Theory for Robot Applications. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 9 (2-8), (2017), pp. 7-11.[28] Brucal, S., Africa, A. & Dadios, E. Female Voice Recognition using Artificial Neural Networks and MATLAB Voicebox Toolbox. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10 (1-4), (2018), pp. 133-138.[29] Africa, A. & Velasco, J. Development of a Urine Strip Analyzer using Artificial Neural Network using an Android Phone. ARPN Journal of Engineering and Applied Sciences, Vol. 12 (6), (2017), pp. 1706-1712.[30] Loresco, P. & Africa, A. ECG Print-out Features Extraction Using Spatial-Oriented Image Processing Techniques. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 10 (1-5), (2018), pp. 15-20.[31] Snort. (2018). https://www.snort.org/. [32] Africa, A. A Logic Scoring of Preference Algorithm using ISO/IEC 25010:2011 for Open Source Web Applications Moodle and Wordpress. ARPN Journal of Engineering and Applied Sciences, Vol. 13 (15), (2018).[33]Suricata. (2018). https://suricata-ids.org/. [34] Gadde, S., Ganta, R., Gupta, A., Rao, R. & Rao, K. Securing Internet of Things (IoT) Using Honey Pots. International Journal of Engineering and Technology, Vol. 7 (2.7), (2018), pp. 820-824.
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Alhussan, Amel Ali, Hassan K. Ibrahim Al Al-Mahdawi, and Ammar Kadi. "Spam Detection in Connected Networks Using Particle Swarm and Genetic Algorithm Optimization: Youtube as a Case study." International Journal of Wireless and Ad Hoc Communication 6, no. 1 (2023): 08–18. http://dx.doi.org/10.54216/ijwac.060101.
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Although there are many networks security tools, both wire and wireless connected networks are still suffering from many types of attacks. YouTube's meteoric rise to prominence as a social platform speaks for itself. The sheer volume of comments on YouTube has made it an ideal medium for spammers to spread their malicious software. Phishing attacks, in which anyone who clicks on a bad link might be a victim, have contributed to this problem. Classification systems may be used to examine spam for its unique characteristics and identify it. This is why it is suggested that YouTube already has built-in mechanisms for identifying spam. A YouTube Spam detection framework was designed with the five stages of data collection, pre-processing, features extraction, classification, and detection, allowing for the execution of the tests. To analyze and validate each stage of the YouTube detection methodology presented in this study, two metaheuristic optimization methods are employed to optimize the parameters of a new voting ensemble classifier. These methods are the particle swarm optimization (PSO) and the Genetic Algorithm (GA). The ensemble model is based on three classifiers: neural. Results indicate that the proposed approach is accurate. In addition, statistical analysis is performed to emphasize the superiority and effectiveness of the proposed methodology.
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Anzhelika, Parkhomenko, Tulenkov Artem, and Zalyubovskiy Yaroslav. "METHOD OF REMOTE MONITORING OF PARAMETERS OF ELECTRICAL SWITCHBOARD EQUIPMENT OF HOME AUTOMATION SYSTEM." Problems of Modeling and Design Automatization, no. 2 (2020): 28–37. http://dx.doi.org/10.31474/2074-7888-2020-2-28-37.
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The paper presents the results of the analysis of electrical switchboards computer-aided design process features. The problems of modeling and ensuring the normal temperature mode of switchboard equipment functioning for safe operation of home automation systems are investigated. The work shows that the shortcomings of existing methods and systems of monitoring make it impossible to use them for remote monitoring of the parameters of switchboard equipment due to a whole set of structural and functional features of developed objects. The main requirements for remote monitoring subsystem are compact modular design of hardware that will save space while providing the necessary functionality, low cost, and low power consumption of the components; reliable transmission of monitoring data over long distances, and data storage for further mining. The results of the development and implementation of the method of remote monitoring of electrical switchboard equipment critical parameters are presented. The possibilities of Orange opensource software using for data visualization and intellectual analysis are shown. The identified associative rules can be used for further improvement of control scenarios of the home automation system and ensure the reliable operation of switchboard equipment. The scientific significance of the work lies in the fact that the method of remote monitoring of operational parameters of electrical switchboard has been further developed based on the developed methodology for determining conditions and types of user notifications as well as the integrated usage of wireless data transmission technologies, cloud storage technologies and data mining tools. The practical significance of the obtained results is that the introduction of the remote monitoring subsystem will increase the reliability and security of the home automation system and its attractiveness to users. Further work will focus on the intellectual analysis of the data obtained based on Bayesian networks to develop and study patterns of human activity during home automation system operation to formulate recommendations for the user and specialists to improve the control scenarios of the home automation system. Key words: home automation system, switchboard equipment, remote monitoring, temperature mode, wireless technologies, cloud service, associative rules.
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Rahim, Muheez A., Bilkisu Jimada-Ojuolape, Monsurat O. Balogun, and Lambe M. Adesina. "Security analysis in wireless networks." Caliphate Journal of Science and Technology 7, no. 1 (2025): 1–11. https://doi.org/10.4314/cajost.v7i1.01.
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Wireless networks have become essential to our daily lives, providing connectivity for a wide range of devices. However, with the increasing use of wireless networks, security has become a major concern. Media Access Control (MAC) address flooding, Dynamic Host Configuration Protocol (DHCP) spoofing, and rogue Secure Shell (SSH) are some of the most common security threats in wireless networks. These attacks can cause loss of confidentiality, integrity, and availability of network resources. In this study, vulnerabilities and challenges related to MAC address flooding, DHCP spoofing, and rogue SSH attacks were investigated. Analysis of the solution to prevent and mitigate these attacks was performed by network simulation using the Cisco Packet Tracer Windows version 8.1.1. The system design would provide a user-friendly interface for network administrators to monitor their networks and check for anomalies. The analysis indicated that Port Security effectively limited unauthorized MAC addresses, DHCP snooping successfully blocked illegitimate DHCP responses, and Access Control Lists restricted rogue SSH access, supporting these methods as optimal solutions for mitigating the respective security threats.
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Joshi, Kapil, Rajesh Kumar, Harishchander Anandaram, et al. "Network security in multiple threats: A critical study." Applied and Computational Engineering 19, no. 1 (2023): 253–58. http://dx.doi.org/10.54254/2755-2721/19/20231053.
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Any organization nowadays needs a secure network. High-speed wireless networks and internet services are becoming increasingly unsafe as a result of the daily rise in security threats. Threats to information security come from within the company, in close to 80% of cases. With the latest trend of mobility (portable devices such as laptops and smartphones etc.), ubiquitous 3G connectivity and this trend increases but as more web-based applications are manufactured and available over the Internet, the instances of insider threats have been rising at an alarming rate. Current or former workers, contractors, or business partners who have permission to access the organization's network and servers are typically the source of insider threats. Confidential information is frequently stolen for monetary gain or wellful harm. Access to hacking tools via the Internet, USB drives, and wireless connectivity make break-ins simple. Here, Millions of dollars in losses in the event of IP theft, and customer / individual information theft are yet too considered. This article provides insight into Insider dangers, attackers, and their rationales and recommendations for organizational-level mitigation methods.
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Chander, Bhanu, Prem Kumar.B, and Kumaravelan . "A Analysis of Machine Learning in Wireless Sensor Network." International Journal of Engineering & Technology 7, no. 4.6 (2018): 185. http://dx.doi.org/10.14419/ijet.v7i4.6.20460.
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Advances in hardware as well as wireless network tools have positioned us at the doorstep of a new-fangled era where undersized wireless devices will endow with access to information every time, everyplace plus enthusiastically contribute in constructing smart atmosphere. The sensors in WSN’s assemble information regarding the substances they are exploited to sense. Nevertheless these sensors are restricted in their performance by restrictions of power plus bandwidth. Machine Learning methods can facilitate them in overcoming such restrictions. During the past decade, WSNs have seen progressively more rigorous implementation of highly developed machine learning algorithms for information handing out and improving network performance. Machine learning enthuse countless realistic clarifications that make best use of resource exploitation along with make longer the existence of the network. In particular, WSN designers have effectively agree to machine learning paradigms to deal with widespread purposeful problems associated to localization, data aggregation, fault detection, Security, node clustering, prediction models and energy aware routing, etc.
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Malekzadeh, Mina, Abdul Azim Abdul Ghani, and Shamala Subramaniam. "Design of Cyberwar Laboratory Exercises to Implement Common Security Attacks against IEEE 802.11 Wireless Networks." Journal of Computer Systems, Networks, and Communications 2010 (2010): 1–15. http://dx.doi.org/10.1155/2010/218271.
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In wireless network communications, radio waves travel through free space; hence, the information reaches any receiving point with appropriate radio receivers. This aspect makes the wireless networks vulnerable to various types of attacks. A true understanding of these attacks provides better ability to defend the network against the attacks, thus eliminating potential threats from the wireless systems. This work presents a series of cyberwar laboratory exercises that are designed for IEEE 802.11 wireless networks security courses. The exercises expose different aspects of violations in security such as confidentiality, privacy, availability, and integrity. The types of attacks include traffic analysis, rogue access point, MAC filtering, replay, man-in-the-middle, and denial of service attacks. For each exercise, the materials are presented as open-source tools along with descriptions of the respective methods, procedures, and penetration techniques.
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Muhammad Faseeh Sultan, Sammia Hira, Sohail Masood Bhatti, and Allah Rakha. "Analysis of Machine Learning Methods for Intrusion Detection Systems in Wireless Networks." Bulletin of Business and Economics (BBE) 13, no. 2 (2024): 391–403. http://dx.doi.org/10.61506/01.00343.
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Wireless networks have become integral to modern communication systems, making them vulnerable to various security threats. Intrusion detection systems (IDSs) are essential for detecting and mitigating these threats and can also powerfully screen network traffic for pernicious activities that are planned to abuse the classification, honesty, realness, and accessibility of the network. Machine learning (ML) and Deep learning (DL) techniques are effective in identifying and classifying network attacks. This study proposes a novel intrusion detection system that employs ML and DL models to classify and distinguish network attacks in wireless networks. The proposed system enhanced detection accuracy and efficiency in IDS, the scalability of IDS systems, and estimated the performance of IDS in wireless networks. It also investigates IDS techniques using machine learning, designs and implements IDS in wireless networks using machine learning, and trains several IDS models regarding wireless networks that are fitted. It contrasts the exhibition of proposed models and existing procedures. The suggested system can therefore be utilized as an effective IDS for wireless networks, providing real-time detection and classification of network attacks.
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Kundu, Vinit. "Beyond Wiretapping: The Power and Perils of Capturing Network Communication." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32042.
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The rise of wireless networks has made life easier and more mobile, but it has also made it easier for people to secretly listen in on conversations, known as "tapping." This study looks at how tapping into wireless networks is done, showing how it can be risky. By closely examining how wireless tapping works, particularly through techniques like monitor mode or promiscuous mode in network interface cards (NICs), we explore how sensitive information can be revealed and put at risk. Additionally, we look into why data leaks might happen, studying the detailed flow of network activity using tools like Wireshark and TCP dump analysis. We aim to identify vulnerabilities that could lead to harmful activities like unauthorized access, data theft, or disruptions to network services. In summary, this research aims to emphasize the risks associated with wireless network tapping and provide advice on how to prevent it. By understanding the tactics employed by attackers, both organizations and individuals can implement measures to safeguard their wireless communication and decrease the likelihood of unauthorized access to critical data. Keywords— tapping, sniffing, intercept, network security.
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Chakkarapani Sumathi, Thilagasree, Jayakumar Thippan, Brainy Joseph Raj Vikilal Joice, Pragathi Subramaniam, and Daekook Kang. "A comprehensive decision assessment for trust evaluation in wireless sensor networks." Yugoslav Journal of Operations Research, no. 00 (2024): 11. http://dx.doi.org/10.2298/yjor240217011c.
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Objective: This study endeavors to tackle the pressing issue of network security within Wireless Sensor Networks (WSNs) through the introduction of an innovative integrated Pythagorean fuzzy-based method for evaluating network nodes. The primary aim is to enrich decision making frameworks within WSNs by furnishing a robust methodology for identifying and eradicating malevolent nodes. Methods: investigation employs an integrated Pythagorean fuzzy-based methodology to appraise network nodes by scrutinizing specific trust attributes. Comparative assessment with alternative contemporary Multiple Criteria Decision Making (MCDM) trust models is conducted to gauge the effectiveness of the proposed strategy. Results: The results underscore the efficacy of the integrated Pythagorean fuzzy-based approach in bolstering network security through the precise identification and elimination of malicious nodes. Comparative analysis with other MCDM trust models highlights the superiority of the proposed approach in evaluating network nodes based on trust attributes. Recommendations: In light of the findings, it is advised to integrate fuzzy decision analysis methodologies into decision making systems for WSNs to enhance network security. Additionally, future research endeavors could concentrate on refining and expanding upon the proposed methodology to effectively address emerging security challenges within WSNs.
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Li, Feng. "Model for evaluating the security of wireless sensor network with fuzzy number intuitionistic fuzzy information." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 3559–73. http://dx.doi.org/10.3233/jifs-211731.
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Many experts and scholars focus on the Maclaurin symmetric mean (MSM) equation, which can reflect the interrelationship among the multi-input arguments. It has been generalized to different fuzzy environments and put into use in various actual decision problems. The fuzzy data intuitionistic fuzzy numbers (FNIFNs) could well depict the uncertainties and fuzziness during the security evaluation of Wireless Sensor Network (WSN). And the WSN security evaluation is frequently viewed as the multiple attribute decision-making (MADM) issue. In this paper, we expand the generalized Maclaurin symmetric mean (GMSM) equation with FNIFNs to propose the fuzzy number intuitionistic fuzzy generalized MSM (FNIFGMSM) equation and fuzzy number intuitionistic fuzzy weighted generalized MSM (FNIFWGMSM) equation in this study. A few MADM tools are developed with FNIFWGMSM equation. Finally, taking WSN security evaluation as an example, this paper illustrates effectiveness of the depicted approach. Moreover, by comparing and analyzing the existing methods, the effectiveness and superiority of the FNIFWGMSM method are further certified.
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Kerrakchou, Imane, Sara Chadli, Yassine Ayachi, and Mohammed Saber. "Modeling the impact of jamming attacks in the internet of things." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1206–15. https://doi.org/10.11591/ijeecs.v26.i2.pp1206-1215.
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Security is a key requirement in the context of the internet of things (IoT). The IoT is connecting many objects together via wireless and wired connections with the goal of allowing ubiquitous interaction, where all components may communicate with others without constraints. The wireless sensor network is one of the most essential elements of IoT concepts. Because of their unattended and radio-shared nature for communication, security is becoming an important issue. Wireless sensor nodes are susceptible to different types of attacks. Such attacks can be carried out in several various ways. One of the most commonly utilized methods is jamming. However, there are also some other attack types that we need to be aware of, such as tampering, and wormhole. In this paper, we have provided an analysis of the layered IoT architecture. A detailed study of different types of jamming attacks, in a wireless sensor network, is presented. The packet loss rate, and energy consumption. are calculated, and the performance analysis of the wireless sensor network (WSN) system is achieved. The protocol chosen to evaluate the performance of the WSN is the sensor-medium access control (S-MAC) protocol. Different simulations are realized to evaluate the performance of a network attacked by the different types of jamming attacks.
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Mubaraq, Ahmad Ridhani, Wawan Joko Pranoto, and Abdul Hallim. "Wireless Network Quality Analysis Using RMA and RSSI Methods at BPKAD Berau District." Journal of Computer Networks, Architecture and High Performance Computing 7, no. 2 (2025): 503–13. https://doi.org/10.47709/cnahpc.v7i2.5718.
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Wireless networks are now essential in supporting government operations, including at the BPKAD office in the Berau district. However, problems like unstable connections and slow speeds often arise as obstacles. This study aims to evaluate the quality of the wireless network in the BPKAD asset room of the Berau district by applying the Reliability, Maintainability, and Availability (RMA) and Received Signal Strength Indication (RSSI). Quantitative research method. The research population is all wireless access points (Wi-Fi) spread across the BPKAD office. The research sample is the asset field room. Data collection methods through observation, RMA measurement, and RSSI measurement. The data that has been collected will be analyzed using the RMA (Reliability, Maintainability, and Availability) and RSSI (Received Signal Strength Indication) methods. The results obtained show that most of the measurement days recorded network availability (availability) of 100%. However, there was a decrease on August 26, 2024 (99.58%) and September 3, 2024 (97.05%) due to the increased frequency of system failures. The analysis of RSSI showed that the signal quality fell into the excellent category with an average of -36.6 dBm. However, a decrease was recorded on August 30, 2024, with a value of -44 dBm. The results of this study underscore the importance of regular maintenance and upgrades to the network infrastructure in anticipation of possible deterioration. Recommendations include improving security systems, hardware updates, and technical training for IT staff to strengthen the network's support of activities at the BPKAD Office of Berau Regency.
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Rusdan, Muchamad, and Muhamad Sabar. "Design and Analysis of Wireless Network with Wireless Distribution System using Multi-Factor Authentication-based User Authentication." Journal of Information Technology 2, no. 1 (2020): 17–24. http://dx.doi.org/10.47292/joint.v2i1.004.
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Wireless networks are one of the best alternatives in building practical and flexible computer networks that have high mobility. Most of them use wireless networks to support existing cable networks, but on wireless networks, they still use cable media as a backbone of the access point, which supports communication users can access the internet and find information. The problem of using cables as backbone media can be a significant challenge in places that are difficult to reach by wires. Wireless networks provide convenience and convenience that is high enough to use. As long as they are in an area that is supported by a wireless network, users can access the internet at any time. To make the wireless network connected to the internet safe and easy to use, we can create a user authentication system based on Multi-Factor Authentication (MFA) that can be used to authenticate and authorize. In general, each user can use existing wireless network services by verifying users based on Wifi Protected Access 2 Pre-Shared Key (WPA2-PSK). The purpose of this research is to develop a wireless network that uses Multi-Factor Authentication (MFA) based user authentication to be able to connect to the wireless network to increase security and provide the use of existing wireless networks. The research method used in this study uses descriptive qualitative research methods, with data collection using literature study and observation techniques. After analysis and design, it can be concluded that user authentication is based on Multi-factor Authentication (MFA) in a safe and user-friendly manner that can determine the users who are allowed and not allowed to use wireless networks.
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Tran, Tung Duong. "Resource Allocation in Air Base Station-Enabled Wireless Network: Survey." Telecom IT 12, no. 2 (2024): 40–47. https://doi.org/10.31854/2307-1303-2024-12-2-40-47.
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Problem statement. Due to their mobility, flexibility, ease of deployment, and low cost, Autonomous Aerial Vehicle (AAV) play an important role in future wireless networks. However, their pratical implementation faces challenges, including energy constraints, dynamic channel variations, interference management, and the need for efficient resource allocation to ensure seamless connectivity for ground users. Traditional optimization methods often fail to adapt to these complexities in real-time, limiting the effectiveness of AAV-assisted wireless networks. The aim of the work provides a comprehensive review of resource allocation in AAV-assisted wireless networks, focusing on power and bandwidth optimization strategies, as well as the key challenges in ensuring efficient and reliable communication. Methods: used in this study include a systematic review of existing literature, analyzing optimization approaches such as Game Theory, Artificial Intelligence for efficient resource allocation in AAV-assisted wireless networks. Novelty: this study analyzes resource allocation challenges in AAV-assisted network, focusing on the interdependence of power and bandwidth allocation. It explores optimization techniques like Game Theory, and Artificial Intelligence. Results. The analysis in this paper demonstrates that Game Theory and Artificial Intelligence based approaches significantly improve resource allocation efficiency. Additionally, the study identifies key challenges, including heterogeneous density network, security concerns, and complex channel modeling, providing insights for future research. Practical / Theoretical Relevance: This study advences the theoretical understanding of resource allocation in AAV-assisted wireless networks by integrating optimization strategies from Game Theory and Artificial Intelligence. Pratically, it provides insight into enhancing network efficiency, adaptability and security, making AAV-based communication more viable for real-world applications, such as disaster recovery, remote areas coverage, and IoT data collection.
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Gowda, V. Dankan, D. Palanikkumar, Sajja Suneel, Satish Dekka, T. Thiruvenkadam, and K. D. V. Prasad. "Cryptographic image-based data security strategies in wireless sensor networks." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 2 (2024): 293–304. http://dx.doi.org/10.47974/jdmsc-1883.
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There is need to develop and create new data protection mechanisms aimed at the compensating for the lack of security of WSNs. This paper introduces new cryptographic image-based techniques aimed at improving data security in WSNs. The solution also integrates imaging processing techniques with advanced cryptographic principles to merge a two-layered security architecture. This work provides a new WSN-specific encryption algorithm that delivers low energy consumption and smaller computational burden. Likewise, this is connected to an image-based authentication approach which employs steganographic methods in the information hiding technique. Intensive simulations allow the approach that supports methodology, naming increased security metrics obtained without decreasing in network performance. This study seeks to fill the gap in current practice in WSN security and provides a foundation for future research on image-based techniques integrated with cryptographic algorithms aiming at more secure data protection under limited resources.
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Kerrakchou, Imane, Sara Chadli, Yassine Ayachi, and Mohammed Saber. "Modeling the impact of jamming attacks in the internet of things." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1206. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1206-1215.
Full textAbstract:
Security is a key requirement in the context of the Internet of Things. The IoT is connecting many objects together via wireless and wired connections with the goal of allowing ubiquitous interaction, where all components may communicate with others without constraints. The wireless sensor network is one of the most essential elements of IoT concepts. Because of their unattended and radio-shared nature for communication, security is becoming an important issue. Wireless sensor nodes are susceptible to different types of attacks. Such attacks can be carried out in several various ways. One of the most commonly utilized methods is Jamming. However, there are also some other attack types that we need to be aware of, such as Tampering, Wormhole, etc. In this paper, we have provided an analysis of the layered IoT architecture. A detailed study of different types of Jamming attacks, in a wireless sensor network, is presented. The packet loss rate, energy consumption, etc. are calculated, and the performance analysis of the WSN system is achieved. The protocol chosen to evaluate the performance of the WSN is the S-MAC protocol. Different simulations are realized to evaluate the performance of a network attacked by the different types of Jamming attacks.
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C. Visalatchi. "Enhancing Network Security in Wireless Sensor Networks through Rsa-Based Secure Memory Management and Optimized Resource Allocation: A Comprehensive Survey." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 2777–97. https://doi.org/10.52783/cana.v32.4555.
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To improve the safety of networks in Wireless Sensor Networks (WSNs), this study offers a thorough analysis and presents a unique architecture that combines the best utilization of resources with RSA-based secure control of memory. WSNs are essential for many tasks, including weapons systems and tracking the environment, but because of their centralized architecture and shortages of resources, they are very susceptible to breaches of security. This assessment provides a critical analysis of current security mechanisms, emphasizing resource distribution tactics, memory administration approaches, and methods of encryption particularly designed for wireless sensor networks. Building on these discoveries, the study suggests a novel paradigm for the secure utilization of memory in sensor networks that makes use of the RSA encryption method to protect information security and secrecy. While upholding stringent safety criteria, the structure uses effective utilization of resources approaches to increase WSNs' overall effectiveness and efficacy. The proposed method provides a strong solution that improves operational effectiveness and security while addressing the particular difficulties presented by WSNs.
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Itagi, Manjunath, Dankan Gowda V, KDV Prasad, Pullela SVVSR Kumar, Shekhar R, and B. Ashreetha. "Performance Analysis of Energy Efficiency and Security Solutions of Internet of Things Protocols." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 442–50. http://dx.doi.org/10.37391/ijeer.110226.
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The scientific and business communities are showing considerable interest in wireless sensor networks (WSN). The availability of low-cost, small-scale components like CPUs, radios, and sensors, which are often combined into a single chip, is crucial. Parallel to the evolution of WSNs, the concepts of the IoT have been evolving in recent years. Wireless communication technologies may play a significant role in the implementation of IoT, despite the fact that IoT does not need or require any particular technology for communication. WSN assisted IoT networks can drive several applications in many industries. The proposed research explores the possibility of enhancing energy efficiency in WSN-assisted IoTN by balancing various challenging sensor network performance metrics. The base station's current placement inside the sensing field is predetermined by the preexisting routing algorithms. Our study examines the impact of base station placement outside and within the prescribed sensing domains on energy consumption and network longevity. In addition, methods for transferring data from the distributed source sensor to the base station while minimizing energy consumption are investigated. In this preliminary study, we focus on developing an algorithm for WSN-Assisted IoTN that can balance network factors such as hop count, communication distance, and residual energy. To further optimize the routing route between local cluster heads and the base station, a novel network architecture is built based on the Ant-optimization model, which uses centroid routing to balance energy consumption among local clusters. An open-source Network Simulator (NS-3) is used to model the behaviour of the proposed routing protocols and compare them to comparable existing network protocols. All of the suggested protocols have the same fundamentals for creating networks, however they vary in terms of routing, optimization, and performance depending on the development effort under consideration.
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ERMOSHINA, Elizaveta E. "The use of neural networks in the analysis of economic security of the university." Economic Analysis: Theory and Practice 22, no. 4 (2023): 732–48. http://dx.doi.org/10.24891/ea.22.4.732.
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Subject. The article considers the introduction of digitalization tools into the process of analyzing the economic security of the university. The growth of technologies related to the use of neural networks, their practical application is currently not adequately developed. Objectives. The aim is to critically review and evaluate the main methods used in the analysis of economic security, develop analytical tools for assessing and forecasting the economic security of higher education institutions based on modern information technology achievements. Methods. The study employs general and specific scientific methods of cognition, methods of data analysis, and machine learning. Results. I reviewed the developed methods of analyzing the economic security of various economic entities and higher educational institutions, underpinned the need to form a complex (integrated) index of economic security of the university, offered a solution to issues related to the choice of methods used for the formation of a complex (integrated) index, the justification of weighting coefficients and threshold values for each index and area of activity of a higher education institution, and interpretation of analysis results. Conclusions. The disclosed formation of the integrated indicator of economic security of the university, the reasoned choice of architecture and method of training of the neural network form the basis for further development of analytical tools to assess economic security, enabling higher efficiency and quality of analysis, and management decisions made on its basis.
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Asonze, Christopher Uzoma, Olumide Samuel Ogungbemi, Favour Amarachi Ezeugwa, Anthony Obulor Olisa, Oluwaseun Ibrahim Akinola, and Oluwaseun Oladeji Olaniyi. "Evaluating the Trade-offs between Wireless Security and Performance in IoT Networks: A Case Study of Web Applications in AI-Driven Home Appliances." Journal of Engineering Research and Reports 26, no. 8 (2024): 411–32. http://dx.doi.org/10.9734/jerr/2024/v26i81255.
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The integration of the Internet of Things (IoT) with artificial intelligence (AI) is transforming home appliances into smarter, more responsive tools that enhance daily living. However, this technological fusion introduces significant security challenges, necessitating a careful balance between security and performance within IoT networks. First, the study answers the question of the trade-offs between security measures and performance metrics in web applications for AI-driven home appliances, and second, how can these trade-offs be optimized to ensure both robust security and high system performance? Using qualitative content analysis, the study identified key security flaws in web application architectures, while quantitative analysis assessed the impact of security protocols on system performance metrics such as latency, throughput, and CPU usage. Atlas.ti and Cisco’s Packet Tracer were utilized for thematic coding and network simulation, respectively, and multivariate regression analysis quantified the influences of security protocols. The results revealed that enhanced security protocols, such as encryption and authentication, significantly impact performance, with encryption increasing latency by an average of 50 milliseconds and reducing throughput by 10% under peak loads. Additionally, CPU usage increased by up to 75% in high-threat scenarios. The proposed security-performance optimization framework dynamically adjusts security measures based on current threat assessments and operational demands, aiming to sustain high performance while ensuring robust security. These findings have real-world applications in the design and implementation of AI-driven home appliances, offering a roadmap for manufacturers to enhance device security without compromising performance. By adopting adaptive security measures and leveraging edge computing, the framework can improve user satisfaction and trust in smart home technologies.
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Sharipuddin, Rafi Septiandi Putra, M. Farhan Aulia, Sayid Achmad Maulana, and Pareza Alam Jusia. "Android Security: Malware Detection with Convolutional Neural Network and Feature Analysis." Media Journal of General Computer Science 1, no. 1 (2023): 7–13. http://dx.doi.org/10.62205/mjgcs.v1i1.7.
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Android is a mobile operating system based on a modified version of the Linux kernel and other open-source tools. Due to its system efficiency and the multitude of features it offers to users, the Android operating system has taken a leading position in the technology market and often attracts the attention of cybercriminals. As malware continues to evolve, traditional methods for detecting Android malware, such as signature-based approaches, may not be sufficient to detect the latest malware threats. Therefore, this research proposes a deep learning algorithm, specifically Convolutional Neural Network (CNN) and Component Analysis (PCA), for feature extraction to enhance the accuracy of Android malware detection. The dataset used in this study is the CICAndMal2017 dataset. Testing results are evaluated using three parameters: accuracy, precision, and recall. Experimental results indicate that our deep learning approach outperforms many other methods with an accuracy of 91%.
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Puinko, L. E., and E. V. Tolkacheva. "Analysis of Threats to Information Security of Public Authorities Using Neural Networks." Modelling and Data Analysis 14, no. 3 (2024): 7–21. http://dx.doi.org/10.17759/mda.2024140301.
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<p>Ensuring the information security of public authorities requires the use of specialized tools that take into account various sources of information threats and their constant changes. This study aims to develop a methodology for analyzing these threats using neural networks. The research uses methods such as machine learning and neural network analysis to systematize the data. The authors adapted the Multi-Layer Perceptron (MLP) architecture and configured the hyperparameters of the neural network to achieve their objectives. The neural network was trained using the Python programming language, and its effectiveness was evaluated using metrics such as accuracy, precision, recall, and F1 score. The results of the study included the development of a method for creating a data set that encompasses assessments of threats to the information security of various public authorities and their sources. Additionally, the study evaluated the effectiveness of neural networks in solving classification problems for public authorities. Finally, the study interpreted the results of neural network analysis to determine the resistance of public authorities against information security threats.</p>
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Savytska, Liudmyla, Tetiana Korobeinikova, Oleksandr Volos, and Mykola Tarnovskyi. "METHOD AND MEANS OF SECURITY MONITORING IN A COMPUTER NETWORK BY SIEM MEANS." Information technology and computer engineering 58, no. 3 (2023): 22–32. http://dx.doi.org/10.31649/1999-9941-2023-58-3-22-32.
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This work focuses on researching, analyzing, and enhancing methods and tools for security monitoring in computer networks. The study develops security monitoring tools and methods based on SIEM agents, improving the data normalization process from security logs. The research explores SIEM's role in the SIEM-EDR-NDR triad perspective to accelerate responses to network security threats. The investigation is grounded in the experiences of foreign companies and domestic banking networks. The interaction of SIEM-EDR-NDR components, forming a SOC triad, is examined. SIEM is utilized for centralized data analysis, including EDR and NDR, providing a comprehensive security overview. EDR detects and responds to threats on endpoints, complemented by NDR, extending SIEM analysis. This combination ensures effective response to cyberattacks, reducing "dwell time" until detection. The formulation of tasks for EDR components in the SIEM-EDR-NDR triad is discussed. Emphasis is placed on the importance of protecting endpoints at all stages of an attack, and effective strategies, such as traffic analysis, application control, and centralized cybersecurity management, are identified. Integration of EDR with existing security tools to create a comprehensive system is highlighted. Within the SIEM context, data processing stages, from log collection and normalization to event classification and correlation, are illuminated. The role of correlation in incident formation and investigation is underscored. An enhanced normalization scheme with an expanded agent deployment and key data processing stages within the SIEM system is proposed. The work addresses the improvement of event log processing in SIEM for effective network security monitoring and timely threat mitigation. The achieved goal accelerates threat response processes through SIEM agent integration, facilitating the organization and classification of information flows for prompt threat mitigation.
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Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.
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The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.
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Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.
Full textAbstract:
The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.
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Sheikh, Muhammad Sameer, and Jun Liang. "A Comprehensive Survey on VANET Security Services in Traffic Management System." Wireless Communications and Mobile Computing 2019 (September 15, 2019): 1–23. http://dx.doi.org/10.1155/2019/2423915.
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Recently, vehicular ad hoc networks (VANETs) embark a great deal of attention in the area of wireless and communication technology and are becoming one of the prominent research areas in the intelligent transportation system (ITS) because they provide safety and precautionary measures to the drivers and passengers, respectively. VANETs are quite different from the mobile ad hoc networks (MANETs) in terms of characteristics, challenges, system architecture, and their application. In this paper, we summarize the recent state-of-the-art methods of VANETs by discussing their architecture, security, and challenges. Secondly, we discuss the detailed analysis of security schemes and the possible measures to provide secure communication in VANETs. Then, we comprehensively cover the authentication schemes, which is able to protect the vehicular network from malicious nodes and fake messages. Thus, it provides security in VANETs. Thirdly, we cover the mobility and network simulators, as well as other simulation tools, followed by the performance of authentication schemes. Finally, we discuss the comfort and safety applications of VANETs. In sum, this paper comprehensively covers the entire VANET system and its applications by filling the gaps of existing surveys and incorporating the latest trends in VANETs.
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Waili, Abdul Rasul AL. "Analysis of Traffic Using the Snort Tool for the Detection of Malware Traffic." April-May 2023, no. 33 (May 24, 2023): 30–37. http://dx.doi.org/10.55529/ijitc.33.30.37.
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The increasing prevalence of malware threats necessitates the development of robust methods for detecting and mitigating malicious network traffic. This paper presents an analysis of traffic using the Snort tool for the detection of malware traffic. The study focuses on understanding traffic patterns, evaluating Snort's performance, and comparing it with other tools or methods for malware detection. The methodology involves data collection, preprocessing, Snort configuration, and traffic analysis. The results reveal valuable insights into traffic patterns associated with malware activities, demonstrate Snort's effectiveness in detecting known malware signatures, and assess its efficiency and scalability. The comparison with other tools provides a comprehensive understanding of Snort's strengths and limitations. This research contributes to the field of network security by providing practical insights for network administrators and suggesting future research directions.
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Ashlesha, S. Nagdive, Tugnayat Rajkishor, and Peshkar Atharva. "Social Network Analysis of Terrorist Networks." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2553–59. https://doi.org/10.35940/ijeat.C5431.029320.
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Terrorist Activities worldwide has led to the development of sophisticated methodologies for analyzing terrorist groups and networks. Ongoing and past research has found that Social Network Analysis (SNA) is most effective method for predictive counter-terrorism. Social Network Analysis (SNA) is an approach towards analyzing the terrorist networks to better understand the underlying structure of a network and to detect key players within the network and their links throughout the network. It is also need of the hour to convert available raw data into valuable information for the purpose of global security. Comparative study among SNA tools testify their applicability and usefulness for data gathered through online and offline social sources. However it is advised to incorporate temporal analysis using data mining methods, to improve the capability of SNA tools to handle dynamic social media data. This paper examine various aspects of Social Network Analysis as applied to terrorism, taking empirical data, and open source data based studies into account. This work primarily focuses on different types of decentralized terrorist networks and nodes. The nodes can be classified as organizations, places or persons. We take help of varied centrality measures to identify key players in this network.
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Asemi, Asefeh, and Fezzeh Ebrahimi. "A Thematic Analysis of the Articles on the Internet of Things in the Web of Science With HAC Approach." International Journal of Distributed Systems and Technologies 11, no. 2 (2020): 1–17. http://dx.doi.org/10.4018/ijdst.2020040101.
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This research was carried out using the bibliometric method to thematically analyze the articles on IoT in the Web of Science with Hierarchical Agglomerative Clustering approach. First, the descriptors of the related articles published from 2002 to 2016 were extracted from WoS, by conducting a keyword search using the “Internet of Things” keyword. Data analysis and clustering were carried out in SPSS, UCINET, and PreMap. The analysis results revealed that the scientific literature published on IoT during the period had grown exponentially, with an approximately 48% growth rate in the last two years of the study period (i.e. 2015 and 2016). After analyzing the themes of the documents, the resulting concepts were classified into twelve clusters. The twelve main clusters included: Privacy and Security, Authentication and Identification, Computing, Standards and Protocols, IoT as a component, Big Data, Architecture, Applied New Techniques in IoT, Application, Connection and Communication Tools, Wireless Network Protocols, and Wireless Sensor Networks.
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Palanisamy, Ramaraj, and Yang Wu. "Users’ attitude on perceived security of enterprise systems mobility: an empirical study." Information & Computer Security 29, no. 1 (2021): 159–86. http://dx.doi.org/10.1108/ics-05-2020-0069.
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Purpose This study/ paper aims to empirically examine the user attitude on perceived security of enterprise systems (ES) mobility. Organizations are adopting mobile technologies for various business applications including ES to increase the flexibility and to gain sustainable competitive advantage. At the same time, end-users are exposed to security issues when using mobile technologies. The ES have seen breaches and malicious intrusions thereby more sophisticated recreational and commercial cybercrimes have been witnessed. ES have seen data breaches and malicious intrusions leading to more sophisticated cybercrimes. Considering the significance of security in ES mobility, the research questions in this study are: What are the security issues of ES mobility? What are the influences of users’ attitude towards those security issues? What is the impact of users’ attitude towards security issues on perceived security of ES mobility? Design/methodology/approach These questions are addressed by empirically testing a security model of mobile ES by collecting data from users of ES mobile systems. Hypotheses were evolved and tested by data collected through a survey questionnaire. The questionnaire survey was administered to 331 users from Chinese small and medium-sized enterprises (SME). The data was statistically analysed by tools such as correlation, factor analysis, regression and the study built a structural equation model (SEM) to examine the interactions between the variables. Findings The study results have identified the following security issues: users’ attitude towards mobile device security issues; users’ attitude towards wireless network security issues; users’ attitude towards cloud computing security issues; users’ attitude towards application-level security issues; users’ attitude towards data (access) level security issues; and users’ attitude towards enterprise-level security issues. Research limitations/implications The study results are based on a sample of users from Chinese SMEs. The findings may lack generalizability. Therefore, researchers are encouraged to examine the model in a different context. The issues requiring further investigation are the role of gender and type of device on perceived security of ES mobile systems. Practical implications The results show that the key security issues are related to a mobile device, wireless network, cloud computing, applications, data and enterprise. By understanding these issues and the best practices, organizations can maintain a high level of security of their mobile ES. Social implications Apart from understanding the best practices and the key issues, the authors suggest management and end-users to work collaboratively to achieve a high level of security of the mobile ES. Originality/value This is an empirical study conducted from the users’ perspective for validating the set of research hypotheses related to key security issues on the perceived security of mobile ES.
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Anggi Rachmawati and Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems." International Transactions on Education Technology (ITEE) 1, no. 1 (2022): 1–9. http://dx.doi.org/10.34306/itee.v1i1.170.
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This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.
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Anggi Rachmawati and Yossaepurrohman. "Analysis of Machine Learning Systems for Cyber Physical Systems." International Transactions on Education Technology (ITEE) 1, no. 1 (2022): 1–9. http://dx.doi.org/10.33050/itee.v1i1.170.
Full textAbstract:
This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.
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Aboelfotoh, Said F., and Noha A. Hikal. "A Review of Cyber-security Measuring and Assessment Methods for Modern Enterprises." JOIV : International Journal on Informatics Visualization 3, no. 2 (2019): 157. http://dx.doi.org/10.30630/joiv.3.2.239.
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Regarding the huge spread of technology among individuals and enterprises, technologies and electronic communications become one of the most important pillars of the operation of small and large enterprises alike, and the source of education and entertainment for individuals, this led to thinking about the risks of reliance on this technology and the impact on the economic index of enterprises market, reputation and the safety of individuals and enterprises, these fears forced the experts and decision-makers to think about information security and develop new methods to measure and assess the level of protection of information and data in enterprises and privacy of individuals. This paper introducing a review of recent cyber-security measuring and assessment methodologies and tools based on industry best practices for the measure and assesses of network security and protection of a modern enterprise data network. The analysis is based on a study the methods for the measurement and assessment of information security at the physical and technical level, penetration testing and identification of weaknesses in the cyber-security system followed and policies used in modern enterprises. A comprehensive description of the strengths, weaknesses, and licensing conditions for tools is presented. Moreover, major security requirements associated with modern enterprises is discussed and analyzed to discover vulnerability in the existing systems and explain the potential impact of this vulnerability.
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KN, Swethaa. "Hidden Device Detector." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem43456.
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Abstract-With the rise in unauthorized surveillance and privacy breaches, detecting hidden devices such as cameras and microphones has become a necessity. This project presents a *software-based mobile application* designed to detect hidden devices using multiple techniques, including *AI-driven image recognition, infrared detection, RF signal analysis and network scanning. Unlike conventional hardware-based detectors, this app leverages smartphone sensors, machine learning, and wireless network analysis to **locate hidden cameras, microphones, and unauthorized recording devices* in various environments. A unique feature of this application is the *SOS functionality, allowing users to report their findings to the nearest authorities anonymously. The app also includes a **jamming feature, which disrupts unauthorized recordings to protect user privacy. This is particularly beneficial for individuals who fear filing complaints in person. Additionally, the app can **analyze network traffic* to detect unusual device behavior, ensuring comprehensive security.By integrating multiple detection methods, the app provides *enhanced accuracy compared to traditional detection tools. It is designed to be lightweight, user-friendly, and compatible with most modern smartphones. This project aims to **empower users with an accessible, software-driven solution* for personal security and privacy protection in an increasingly surveillance-prone world. Keywords Hidden device detection, AI-based anomaly detection, RF scanning, infrared camera analysis, privacy protection, SOS emergency feature, mobile security, surveillance jamming, unauthorized recording prevention.
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Herman, Rusyadi Umar, and Agus Prasetyo. "Analysis of Address Resolution Protocol Poisoning Attacks on Mikrotik Routers Using Live Forensics Methods." International Journal of Engineering Business and Social Science 3, no. 4 (2025): 1–18. https://doi.org/10.58451/ijebss.v3i4.231.
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The rapid development of wireless technology has made network communication more accessible but also increasingly vulnerable to security threats. One of the major threats is the Man-in-the-Middle (MitM) Attack, particularly ARP Spoofing, which manipulates the Address Resolution Protocol (ARP) to intercept or alter network traffic. ARP Spoofing, also known as ARP Poisoning, allows attackers to associate incorrect MAC addresses with IP addresses, enabling unauthorized access and potential data interception. This research focuses on the detection and investigation of ARP Spoofing on MikroTik routers using live forensic methods. The study utilizes Wireshark as a primary tool to monitor ARP-based network activity and identify anomalies indicative of ARP Spoofing attacks. The National Institute of Standards and Technology (NIST) forensic framework, which includes Collection, Examination, Analysis, and Reporting, is employed as a methodology for analyzing forensic evidence. The research also incorporates a virtualized attack simulation environment using VirtualBox, where a PC Client acts as the target, an attacker PC executes an ARP Spoofing attack using Ettercap, and Wireshark captures network traffic for forensic examination. The simulation results reveal that an ARP Spoofing attack can successfully manipulate network traffic by altering ARP table entries. The attacker assumes the identity of IP Address 192.168.0.1 with MAC Address e8-cc-18-41-3f-fb, while the target’s identity is duplicated as 192.168.0.19 with MAC Address 08:00:27:15:4c:3c, as confirmed through Wireshark analysis and ARP table inspection using the command prompt. These findings emphasize the importance of implementing proactive security measures, such as Dynamic ARP Inspection (DAI), encryption protocols, and continuous network monitoring, to mitigate the risks associated with ARP Spoofing attacks.
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Chen, Jialong, Jingjing Xiao, and Jiaxin Xu. "VGGIncepNet: Enhancing Network Intrusion Detection and Network Security through Non-Image-to-Image Conversion and Deep Learning." Electronics 13, no. 18 (2024): 3639. http://dx.doi.org/10.3390/electronics13183639.
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This paper presents an innovative model, VGGIncepNet, which integrates non-image-to-image conversion techniques with deep learning modules, specifically VGG16 and Inception, aiming to enhance performance in network intrusion detection and IoT security analysis. By converting non-image data into image data, the model leverages the powerful feature extraction capabilities of convolutional neural networks, thereby improving the multi-class classification of network attacks. We conducted extensive experiments on the NSL-KDD and CICIoT2023 datasets, and the results demonstrate that VGGIncepNet outperforms existing models, including BERT, DistilBERT, XLNet, and T5, across evaluation metrics such as accuracy, precision, recall, and F1-Score. VGGIncepNet exhibits outstanding classification performance, particularly excelling in precision and F1-Score. The experimental results validate VGGIncepNet’s adaptability and robustness in complex network environments, providing an effective solution for the real-time detection of malicious activities in network systems. This study offers new methods and tools for network security and IoT security analysis, with broad application prospects.
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Fan, Xin, and Yan Huo. "Cooperative secure transmission against collusive eavesdroppers in Internet of Things." International Journal of Distributed Sensor Networks 16, no. 6 (2020): 155014772093346. http://dx.doi.org/10.1177/1550147720933464.
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As Internet of Things (IoT) has boomed in recent years, many security issues have also been exposed. Focusing on physical layer security in wireless Internet of Things network communication, a series of security methods have been widely studied. Nevertheless, cooperative jamming methods in physical layer security to fight against collusive eavesdroppers have not been thoroughly studied yet. In this article, we study a cooperative-jamming-based physical layer secure transmission scheme for Internet of Things wireless networks in the presence of collusive eavesdroppers. We design a cooperative jamming strategy without knowing the channel state information of eavesdroppers. Considering the cooperation of multiple nodes with multiple antennas, this strategy can maximize the signal-to-interference-plus-noise ratio at an actuator (legitimate receiver). Meanwhile, the generated cooperative jamming signals can reduce the signal-to-interference-plus-noise ratio at eavesdroppers. To explore the theoretical security performance of our strategy, we perform a secrecy outage probability analysis and an asymptotic analysis. In the cases of cooperative jamming and without cooperative jamming, the closed-form expressions of the secrecy outage probability are deduced, and the influence of system parameters on the secrecy outage probability becomes more intuitive through a strict mathematical asymptotic behavior analysis. In addition, considering the energy limitation of Internet of Things devices, we propose a power allocation algorithm to minimize the total transmission power given the security requirements. The numerical results show the effectiveness of our schemes and are consistent with the theoretical analysis.
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Dr. Abhinandan Singh Dandotiya, Palash Sharma, Bharti gole, Shruti Dubey, and Dr. Nidhi Dandotiya. "An Empirical Analysis of DDoS Attack Detection and Mitigation Techniques: A Comparative Review of Tools and Methods." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1099–108. https://doi.org/10.32628/cseit2410462.
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The availability and integrity of online services and networks are seriously threatened by distributed denial of service (DDoS) assaults. As a result, a plethora of detection and mitigation strategies have been created, all utilizing different instruments and approaches. By comparing various tools and approaches, this study provides an empirical examination of DDoS attack detection and mitigation strategies. We carefully assess the performance of top DDoS detection and mitigation solutions against a range of attack vectors and in diverse network contexts, taking into account their efficacy, efficiency, and accuracy. We examine the benefits, drawbacks, and real-world applications of both open-source and proprietary technologies for network defenders. Additionally, we look at how these tools and techniques adapt to changing threats and talk about new trends and difficulties in the DDoS assault arena. For network security practitioners, researchers, and policymakers looking for an understanding of the state of DDoS defence mechanisms and tactics for strengthening resilience against these disruptive cyber threats, this comparative assessment is an invaluable resource.
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VIKULOV, Sergei F., and Sergei V. STULOV. "Conceptual approaches to ensuring the system of military-economic security of the CSTO member states in the new geopolitical conditions." National Interests: Priorities and Security 18, no. 3 (2022): 408–25. http://dx.doi.org/10.24891/ni.18.3.408.
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Subject. This article considers military-economic security as a category of military-economic analysis. Objectives. The article aims to find the best way to integrate the military capabilities of the Collective Security Treaty Organization (CSTO) member States. Methods. For the study, we used a comparative analysis. Results. The article proposes certain principles of creating an integrated system for ensuring the military-economic security of Russia and its allies. Conclusions. Military-economic partnership involves long-term coordination of the interests of the participants and the use of network coordination tools.
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Khan, Shafiullah, Muhammad Altaf Khan, and Noha Alnazzawi. "Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks." Sensors 24, no. 5 (2024): 1641. http://dx.doi.org/10.3390/s24051641.
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Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs’ learning capabilities to model the network’s dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system’s ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system’s performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.
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Subhash Singh Parihar. "Distributed and Hierarchical Clustering in Wireless Camera Sensor Networks leading to Internet of Things." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 100–117. https://doi.org/10.52783/jisem.v10i13s.2010.
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The Wireless Sensor Industry is crucial for modern applications like military navigation, home automation, and agricultural security. The effective estab-lishment and operation of Wireless Sensor Networks (WSNs) hinge on maxim-izing efficiency, network longevity, and minimizing power consumption while ensuring reliability. Researchers focus on optimizing these aspects to enhance WSN performance. This study explores various strategies for improving power consumption, energy efficiency, and network lifespan in WSNs. It emphasizes distributed and hierarchical clustering methods for their scalability, stability, ef-ficiency, and cost-effectiveness. The performance analysis of different non-meta clustering schemes considers metrics such as alive nodes, dead nodes, and throughput over time. Starting with an overview of WSNs, the paper ad-dresses design issues across different network types, including camera-based and ad-hoc networks, and compares clustering techniques. The conclusion advocates for cooperative communication and integrating clustering schemes to advance Wireless Camera Sensor Networks (WCSNs) for Internet of Things (IoT) ap-plications, ensuring efficient power management, extended lifespan, and bal-anced load distribution, thereby enhancing IoT deployments.
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Nevliudov, Igor, Andrii Sliusar, Sofiia Khrustalova, and Kirill Khrustalev. "AUTOMATED RESOURCE MANAGEMENT SYSTEM FOR THE UTILITY SECTOR BASED ON WIRELESS SENSOR NETWORKS." Innovative Technologies and Scientific Solutions for Industries, no. 1 (23) (April 20, 2023): 184–96. http://dx.doi.org/10.30837/itssi.2023.23.184.
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The subject of this study is methods, tools and automated resource management systems for the housing and communal sector. The object of research is the process of controlling resource consumption at housing and communal facilities. The aim of the study is to develop an automated resource management system for the utility sector based on wireless sensor networks. To achieve this goal, the following tasks were solved: a review and analysis of existing methods, tools and automated resource management systems; selection of system components based on technical requirements and taking into account the selected LoRaWAN wireless connection technology; development of a structural diagram and algorithm for the operation of an automated resource management system based on wireless sensor networks; modelling of the process of managing the resources of the utility sector using a wireless sensor network based on t The following methods are used in the work: critical analysis of LoRa technology and other wireless IoT technologies, FOREL and -means clustering methods. The following results were obtained: a general description of the automated resource management system was carried out, its composition and main tasks were determined, and technical requirements for it were established, wireless data transmission technology was selected, on the basis of which the automated resource management system was built, an in-depth comparative analysis of the most effective modern wireless technologies – LoRaWAN and NB-IoT – was carried out, system components were selected, a structural diagram and algorithm for the automated resource management system were developed, and the process of the automated resource management system was modelled. Conclusions: the application of the proposed automated resource management system provides high-quality control of energy consumption at the facilities of the housing and communal sector, makes it possible to control their volume, monitor and analyse energy consumption data, and manage the entire energy supply network as a single system, which is especially necessary in martial law. This approachallows rationalising the consumption of resources by household consumers, which means that the financial costs of energy supply will decrease and the level of energy savings in the country will increase.