Academic literature on the topic 'Study of wireless network security analysis methods and tools'

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretically exact matching feature are still relevant when creating information security systems such as network intrusion detection systems, antivirus, anti-spam, and wormcontainment systems. The real time multi-pattern string matching task has been a major performance bottleneck in such systems. To speed up the recognition process, developers use a reconfigurable hardware platform based on FPGA devices. Such platform provides almost software flexibility and near-ASIC performance. The most important component of a signature-based information security system in terms of efficiency is the recognition module, in which the multipattern matching task is directly solved. It must not only check each byte of input data at speeds of tens and hundreds of gigabits/sec against hundreds of thousand or even millions patterns of signature database, but also change its structure every time a new signature appears or the operating conditions of the protected system change. As a result of the analysis of numerous examples of the development of reconfigurable information security systems, three most promising approaches to the construction of hardware circuits of recognition modules were identified, namely, content-addressable memory based on digital comparators, Bloom filter and Aho–Corasick finite automata. A method for fast quantification of components of recognition module and the entire system was proposed. The method makes it possible to exclude resource-intensive procedures for synthesizing digital circuits on FPGAs when building complex reconfigurable information security systems and their components. To improve the efficiency of the systems under study, structural-level combinational methods are proposed, which allow combining into single recognition device several matching schemes built on different approaches and their modifications, in such a way that their advantages are enhanced and disadvantages are eliminated. In order to achieve the maximum efficiency of combining methods, optimization methods are used. The methods of: parallel combining, sequential cascading and vertical junction have been formulated and investigated. The principle of multi-level combining of combining methods is also considered and researched. Algorithms for the implementation of the proposed combining methods have been developed. Software has been created that allows to conduct experiments with the developed methods and tools. Quantitative estimates are obtained for increasing the efficiency of constructing recognition modules as a result of using combination methods. The issue of optimization of reconfigurable devices presented in hardware description languages is considered. A modification of the method of affine transformations, which allows parallelizing such cycles that cannot be optimized by other methods, was presented. In order to facilitate the practical application of the developed methods and tools, a web service using high-performance computer technologies of grid and cloud computing was considered. The proposed methods to increase efficiency of matching procedure can also be used to solve important problems in other fields of science as data mining, analysis of DNA molecules, etc. Keywords: information security, signature, multi-pattern matching, FPGA, structural combining, efficiency, optimization, hardware description language.

In terms of security, proactively identifying and mitigating network assaults continues to be essential to the protection of vital systems. This chapter presents a unique method for real-time network intrusion detection that makes use of the resilient gradient boosting technique known as the CatBoost algorithm. Conventional signature-based techniques are inadequate against advanced threats because they frequently fail to adjust to changing assault patterns. Because ML (machine learning), and specifically CatBoost, can handle high-dimensional data, nonlinear relationships, and categorical variables, it appears to be a promising answer. This study uses CatBoost to create an advanced model that can recognize complex patterns in network traffic data. Using a large dataset that includes both attack and regular cases, the system gains the ability to identify minute changes in network behavior that could be signs of impending danger. Through feature importance analysis, one can gain a deeper understanding of the critical elements that contribute to the detection of attacks and, consequently, improve network vulnerabilities. Accuracy, F1-score, recall, and precision are among the measures applied to evaluate the efficacy of the proposed CatBoost-based IDS (intrusion detection system). The CatBoost algorithm is superior to traditional methods in terms of successfully recognizing known and unknown attack types while avoiding false positives, as demonstrated by comparison analyses. In addition, it shows that the model is resilient to evasion tactics used by bad actors, which guarantees strong network infrastructure security. The results highlight the effectiveness of using CatBoost in network security applications and highlight the technology's potential as a preventative measure against online attacks that are constantly changing. More flexible and effective intrusion detection systems are made possible by this research, which advances machine learning-driven strategies for strengthening network defenses.

Computer hackers, both individually and as a group, have been identified as a primary threat to computer systems, users, and organizations. Although hacker groups are complex socio-technical systems, much extant research on hackers is conducted from a technical perspective and at an individual level of analysis. This research proposes a research model composed of five dimensions and their relations in order to study hacker’s social organization in the whole socio-technical context. Based on this model, the researcher applies network analysis methods to disclose the structure and patterns of a significant and complex hacker group, Shadowcrew. Network analysis tools are applied for data processing and data analysis. Three network measures: degree centrality, cognitive demand, and eigenvector centrality, are utilized to determine the critical leaders. Out-degree centrality is employed to analyze the relations among the five dimensions in the research model.

Social media analytics are increasingly incorporated into security practices due to the rise in online criminal and extremist activities. Social media research, however, has not become established in either intelligence practice or academic-based approach. This chapter aims to fill the gap by discussing collection methods and analytical tools for the study of social media data for intelligence and countering violent extremism: social network analysis, sentiment analysis, multilingual analysis, geo-coding, automated entity extraction, semantic search, and multimedia analysis. While technological capabilities of social media analytics are improving rapidly, it needs to be complemented with nuanced perspectives from the social sciences. Understanding of the epistemology of social media and dynamics between the online-offline interaction as well as data access will put practitioners in a better position to reap the benefits of the social media. Attention should be given to train practitioners in relevant technological skills while also incorporating social science knowledge.

Critical structures are increasingly susceptible to cyber threats, leading to the inadequacy of traditional security measures. This study explores the application of deep learning tools aimed specifically at enhancing the security of vital systems, such as power grids and transportation networks. By employing convolutional neural networks and recurrent neural networks, the research conducts an analysis of network traffic data to identify security anomalies. An inclusive dataset, which includes simulated attack scenarios, with workable models. Preliminary findings indicate that the convolutional neural network model achieved a high level of anomaly detection accuracy, ominously surpassing the performance of conventional methods. Furthermore, the recurrent neural network model demonstrated enhanced capabilities in detecting time-sensitive threats, resulting in an extensive reduction in false positives. This chapter focus on future research should focus on examining the integration of deep learning models with security frameworks to fortify resilience against evolving threats

Progress in wireless systems has enabled the creation of low-cost, ergonomic, multi-functional, miniature sensing devices. These devices come together in large numbers creating wireless sensor networks (WSNs), which serve for sensing, collecting, analyzing, and sending detected data to a base station. Problems arise, however, due to the limitations of sensor nodes (SNs), incorrect aggregation of data, redundant and similar data problems, data security and reliability, and some others related to WSN topology. This chapter proposes a novel method for solving WSNs problems to improve cyber-physical systems (CPS). As WSN is of increasing interest in CPSs, the authors put forward an approach for reconstructing WSNs. For traditional methods are not able to cope with such problems, this study takes up rendering WSNs more functional through artificial intelligence (AI) techniques which are considered to develop smart SNs through “intelligent computing,” “deep learning,” “self-learning,” and “swarm learning” ability on the network to improve functionality, utility, and survivability of WSNs.

This section presents the results of the first stage of research aimed at developing the conceptual foundations and experimental model of a robotic sensor network for monitoring emergencies and demining demilitarized zones. The main attention is paid to the creation of new principles, models and methods of functioning of sensor networks that operate on the basis of secure wireless communication protocols. An approach to the synthesis of cryptographic algorithms and noise-resistant control protocols optimized for operation in electronic warfare conditions is proposed. The section considers the theoretical foundations of the system, develops an experimental model of the network, and analyzes its functioning under the influence of external destabilizing factors. The results of the study allow significantly increasing the efficiency and stability of robotic sensor networks in difficult operating conditions. The section will be useful for scientists, engineers, as well as specialists working in the fields of information technology, robotics, and national security. The purpose of this research is Development of conceptual foundations, theoretical models and an experimental model of a robotic sensor network designed for emergency monitoring and demining of demilitarized zones, with an emphasis on the implementation of secure wireless communication protocols, optimization of cryptographic data protection and ensuring network resilience to destabilizing external factors. The research methodology of the first stage is based on an interdisciplinary approach that combines elements of systems analysis, control theory, cryptography, and artificial intelligence. The main focus is on developing a conceptual model of a robotic sensor network capable of operating in conditions of destabilizing external influences, such as electronic warfare and complex landscape conditions. The first stage was the formulation of key requirements for the system, in particular its security, energy, and communication parameters. For this purpose, an analysis of existing methods and technologies, their limitations, and integration capabilities was conducted. Based on this, a theoretical framework was developed that includes mathematical models of the sensor network, data transmission algorithms, and methods for their cryptographic protection. The next stage was the modeling and optimization of processes in the sensor network using machine learning and fuzzy logic technologies. Particular attention was paid to the creation of a noise-resistant control protocol that ensures effective operation of the network even in difficult conditions. In addition, methods for testing statistical characteristics of data transmission protocols were developed, which allow assessing their efficiency and stability. The last step within this stage was the creation of an experimental sample of the sensor network. This sample was tested for compliance with the criteria for resistance to external influences, including radio-electronic countermeasures, which provided a practical verification of the developed models and algorithms. The results of the study became the basis for further improvement of the system and its adaptation to real operating conditions. The scientific novelty of this section presents fundamentally new approaches to building robotic sensor networks focused on monitoring emergencies and demining in demilitarized zones. For the first time, the concept of using noise-resistant wireless communication protocols has been developed and substantiated, which provide high resistance to destabilizing external influences, such as electronic warfare. A new method of verifying the distributed knowledge base of the network has been proposed, based on the principle of the "wandering core", which allows to increase the reliability of the transmitted information. One of the key aspects of scientific novelty is the synthesis of artificial intelligence models for network control and decision-making in real time. The developed algorithms for adapting the system to changing conditions allow to ensure stable operation of the network even in the event of significant loss of nodes or disruption of communication channels. This significantly expands the possibilities of using such systems in difficult conditions, in particular in areas of active enemy countermeasures. New graph models have also been developed to describe the functioning of the sensor network, which provide parallel formal analysis of fuzzy situations. The use of these models allows obtaining a wide range of system stability characteristics, which makes it effective for monitoring and demining tasks under conditions of uncertainty. The results of the study create a new scientific platform for further improvement of wireless communication technologies, artificial intelligence and automated monitoring systems. This contributes not only to the development of theory, but also creates practical prerequisites for the use of such systems in the field of national security and defense. Results. The conceptual foundations of a robotic sensor network that ensures functional stability in difficult operating conditions were developed within the section. The main requirements for the system were formulated, including its energy efficiency, security parameters, and the ability to reliably transmit data in an environment with a high level of radio-electronic interference. Mathematical models were developed to describe the functioning of the sensor network and a noise-resistant communication control protocol was proposed. Data transmission protocols were simulated and tested, which made it possible to assess their efficiency and stability in conditions of destabilizing external influences. Artificial intelligence algorithms were proposed, aimed at increasing the autonomy of the sensor network. The algorithms allow the system to adapt to changes in the external environment and maintain functionality even in the event of partial loss of nodes. Special attention is paid to methods for training the system to increase accuracy and speed of response. An experimental sample of a robotic sensor network was implemented. Tests were conducted to confirm the compliance of the developed models and protocols with the requirements of stability, functional reliability and security in conditions of electronic countermeasures. The system's resistance to various destabilizing factors, such as radio interference, landscape changes and loss of individual network components, was assessed. The proposed solutions demonstrate high reliability for use in emergency monitoring and demining tasks. The results obtained make a significant contribution to the development of modern sensor network technologies and their practical application in the fields of defense, security and robotics. They also create a basis for further improvement and implementation of the proposed solutions.

Abstract: Digital forensics is a critical field within cybersecurity, dedicated to the investigation and analysis of digital evidence in the context of cybercrimes and security incidents. This research article explores the theoretical foundations, methodologies, and practical applications of digital forensics. Through comprehensive analysis, the study identifies key techniques used in digital investigations, significant case studies, and the challenges faced by forensic experts. The findings highlight the importance of robust forensic methods, the role of emerging technologies, and the legal implications of digital evidence. Practical recommendations for forensic practitioners, law enforcement, and policymakers are provided, along with suggestions for future research to further advance the field. Keywords: Digital Forensics, Cyber Evidence, Cybersecurity, Digital Investigations, Data Recovery, Forensic Analysis, Cybercrime, Computer Forensics, Network Forensics, Mobile Device Forensics, Evidence Collection, Digital Evidence, Incident Response and Cyber Threats. References: Digital Forensics of Cybercrimes and the Use of Cyber Forensics Tools to Obtain Digital Evidence. (2021). Advances in Digital Crime, Forensics, and Cyber Terrorism, 45–68. https://doi.org/10.4018/978-1-7998-4162-3.ch003 Kävrestad, J. (2020). Cybercrime, Cyber Aided Crime, and Digital Evidence. Fundamentals of Digital Forensics, 59–62. https://doi.org/10.1007/978-3-030-38954-3_6 Khan, M. Z., Mishra, A., & Khan, M. H. (2020). Cyber Forensics Evolution and Its Goals. Critical Concepts, Standards, and Techniques in Cyber Forensics, 16–30. https://doi.org/10.4018/978-1-7998-1558-7.ch002 Luthfi, A., & Prayudi, Y. (2015). Process Model of Digital Forensics Readiness Scheme (DFRS) as a Recommendation of Digital Evidence Preservation. 2015 Fourth International Conference on Cyber Security, Cyber Warfare, and Digital Forensic (CyberSec). https://doi.org/10.1109/cybersec.2015.31 Marcella, A. J., & Guillossou, F. (Eds.). (2012). Cyber Forensics. https://doi.org/10.1002/9781119203452 Mobile Forensics: Beyond Traditional Sources of Digital Evidence. (2020). Proceedings of the 19th European Conference on Cyber Warfare. https://doi.org/10.34190/ews.20.042 Naidu (Retd.), Gp. C. P. A. (2021). Cyber Crime Intervention- Attacks Analysis for Digital Forensics. Digital Forensics (4n6) Journal. https://doi.org/10.46293/4n6/2021.03.02.02 Othman, R. (2020). Financial Statement Fraud Detection and Investigation in Digital Environment. Critical Concepts, Standards, and Techniques in Cyber Forensics, 187–214. https://doi.org/10.4018/978-1-7998-1558-7.ch011

This paper presents a multidisciplinary approach to analyzing data from Telegram for early warning information regarding cyber threats. With the proliferation of hacktivist groups utilizing Telegram to disseminate information regarding future cyberattacks or to boast about successful ones, the need for effective data analysis methods is paramount. The primary challenge lies in the vast number of channels and the overwhelming volume of data, necessitating advanced techniques for discerning pertinent risks amidst the noise. To address this challenge, we employ a combination of neural network architectures and traditional machine learning algorithms. These methods are utilized to classify and identify potential cyber threats within the Telegram data. Additionally, sentiment analysis and entity recognition techniques are incorporated to provide deeper insights into the nature and context of the communicated information. The study evaluates the effectiveness of each method in detecting and categorizing cyber threats, comparing their performance and identifying areas for improvement. By leveraging these diverse analytical tools, we aim to enhance early warning systems for cyber threats, enabling more proactive responses to potential security breaches. This research contributes to the ongoing efforts to bolster cybersecurity measures in an increasingly interconnected digital landscape.

Ubiquitous computing analysis represents an emerging area that implements communication technologies in day-to-day life actions. Ubiquitous computing changes the methods in which people use computers, considering these are involved in everyday aspects. In ubiquitous computing, several types of processes operate automatically in the background and communicate on the user's account. The ubiquitous computing theory is to provide any information for everyone at anytime and anywhere instantly. Mobile ad-hoc Networks are currently a growing technology for the next generation of wireless communication networks. A mobile ad-hoc network can portray as a military or rescue operation network in which a set of mobile nodes are used to send out a mission operation in diplomatic terms. This paper presents common architecture principles of universal systems and analyses significant features in context-aware ubiquitous systems. The main purpose of this work is to define a principle for researchers who are new to ubiquitous computing and want to gain depth on analysis and implementation of a novel method for the ubiquitous computing system in military sectors, in order to contribute towards further research regulations expected into the quality-of-service pledge of ubiquitous computing. The ubiquitous computing is the future of technology related to the internet or smart devices. Moreover, the applicability of technologies found in smartphones, smart TVs and sensors can guide to an increase in strategic capabilities, like sensing and detecting, exchanging and sharing unique real-time data in the military field. This study aims to sketch particular methods by which the learning and teaching methods can be improved. The intelligence of technology advanced in the military sectors along with ubiquitous computing providing a rise in optimization, security, and defence.