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Journal articles on the topic 'Network Intrusion Prevention'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Kaur, Harpreet. "NETWORK INTRUSION DETECTION AND PREVENTION ATTACKS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (2012): 21–23. http://dx.doi.org/10.24297/ijct.v2i3a.2669.

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Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathw

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Musambo, Lubasi Kakwete, Melissa K. Chinyemba, and Jackson Phiri. "Identifying Botnets Intrusion & Prevention – A Review." Zambia ICT Journal 1, no. 1 (2017): 63–68. http://dx.doi.org/10.33260/zictjournal.v1i1.28.

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Systems and networks will be compromised, almost always regardless of what network engineers do. It is then paramount to install and manage systems that have a capacity to identify all forms of intrusions and possibly prevent those intrusions and related attacks on a computer network especially bots. Bots are at the midpoint of most network problems because almost all major cybercrimes and breaches can be traced back to them. This paper reviews three ways in which an intrusion may occur on networks with a focus on botnets and ways in which botnet mitigation may be enhanced.

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Ms. Hemangni Mehta, Ms. Anjali Nizama, and Ms. Subhashini K. "Comprehensive Review of Network Intrusion Detection and Prevention Systems." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 6 (2025): 804–7. https://doi.org/10.51583/ijltemas.2025.140600087.

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Abstract— The global network infrastructure remains vulnerable and is susceptible to attacks from various sources. These attacks can take the form of denial-of-service (DoS) or other malicious threats. To safeguard such networks, Intrusion Detection and Prevention Systems (IDPS) are employed. These systems serve as critical security mechanisms designed to detect and prevent both internal and external threats.IDPS continuously monitor network traffic using a variety of techniques. When suspicious or malicious activity is detected, the system blocks the threat and generates alerts for further in

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Aranganathan, A., and C. D. Suriyakala. "Agent based secure intrusion detection and prevention for rushing attacks in clustering MANETs." International Journal of Engineering & Technology 7, no. 2.20 (2018): 22. http://dx.doi.org/10.14419/ijet.v7i2.20.11736.

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Intrusion detection is one of challenging issues in wireless networks. The inherently vulnerable characteristics of wireless mobile ad hoc networks make them susceptible to attacks in-spite of some security measures, and it may be too late before any counter action can take effect. As such, there is a need to complement security mechanisms with efficient intrusion detection and response systems. This paper proposes an agent-based model to address the aspect of intrusion detection in cluster based Mobile ad hoc network environment. The model comprises of mobile agents, which are used to detect

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Singh, Neha, Deepali Virmani, and Xiao-Zhi Gao. "A Fuzzy Logic-Based Method to Avert Intrusions in Wireless Sensor Networks Using WSN-DS Dataset." International Journal of Computational Intelligence and Applications 19, no. 03 (2020): 2050018. http://dx.doi.org/10.1142/s1469026820500182.

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Intrusion is one of the biggest problems in wireless sensor networks. Because of the evolution in wired and wireless mechanization, various archetypes are used for communication. But security is the major concern as networks are more prone to intrusions. An intrusion can be dealt in two ways: either by detecting an intrusion in a wireless sensor network or by preventing an intrusion in a wireless sensor network. Many researchers are working on detecting intrusions and less emphasis is given on intrusion prevention. One of the modern techniques for averting intrusions is through fuzzy logic. In

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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock

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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock

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8

Patidar, Sadhana, Priyanka Parihar, and Chetan Agrawal. "A Review of Intrusion Detection Datasets and Techniques." SMART MOVES JOURNAL IJOSCIENCE 6, no. 3 (2020): 14–22. http://dx.doi.org/10.24113/ijoscience.v6i3.277.

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As network applications grow rapidly, network security mechanisms require more attention to improve speed and accuracy. The evolving nature of new types of intrusion poses a serious threat to network security: although many network securities tools have been developed, the rapid growth of intrusive activities is still a serious problem. Intrusion detection systems (IDS) are used to detect intrusive network activity. In order to prevent and detect the unauthorized access of any computer is a concern of Computer security. Hence computer security provides a measure of the level associated with Pr

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Ninawe, Shreyash, Vilas Bariyekar, and Ranjita Asati. "Network Intrusion Prevention System." IJARCCE 8, no. 2 (2019): 196–99. http://dx.doi.org/10.17148/ijarcce.2019.8235.

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Sharma, Himanshu, Prabhat Kumar, and Kavita Sharma. "Recurrent Neural Network based Incremental model for Intrusion Detection System in IoT." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3778–95. http://dx.doi.org/10.12694/scpe.v25i5.3004.

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The security of Internet of Things (IoT) networks has become a integral problem in view of the exponential growth of IoT devices. Intrusion detection and prevention is an approach ,used to identify, analyze, and block cyber threats to protect IoT from unauthorized access or attacks. This paper introduces an adaptive and incremental intrusion detection and prevention system based on RNNs, to the ever changing field of IoT security. IoT networks require advanced intrusion detection systems that can identify emerging threats because of their various and dynamic data sources. The complexity of IoT

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Anand, Saloni, and Kshitij Patne. "Network Intrusion Detection and Prevention." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3754–59. http://dx.doi.org/10.22214/ijraset.2022.44761.

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Abstract: Intrusion Detection systems are now increasingly significant in network security. As the number of people using the internet grows, so does the chance of a cyberattack. People are adopting signature-based intrusion detection systems. Snort is a popular open-source signature-based intrusion detection system. It is widely utilised in the intrusion detection and prevention arena across the world. The aim of this research is to provide knowledge about intrusion detection systems, application vulnerabilities, and their prevention methods and to perform a comparison of the latest tools and

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Javidi, Mohammad Masoud. "Game Theory Approaches in Taxonomy of Intrusion Detection for MANETs." Computer Engineering and Applications Journal 4, no. 1 (2015): 31–42. http://dx.doi.org/10.18495/comengapp.v4i1.111.

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MANETs are self configuring networks that are formed by a set of wireless mobile nodes and have no fixed network infrastructure nor administrative support. Since transmission range of wireless network interfaces is limited, forwarding hosts may be needed. Each node in a wireless ad hoc network functions is as both a host and a router. Due to their communication type and resources constraint, MANETs are vulnerable to diverse types of attacks and intrusions so, security is a critical issue. Network security is usually provided in the three phases: intrusion prevention, intrusion detection and in

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Chandre, Pankaj Ramchandra, Parikshit Mahalle, and Gitanjali Shinde. "Intrusion prevention system using convolutional neural network for wireless sensor network." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 504. http://dx.doi.org/10.11591/ijai.v11.i2.pp504-515.

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Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learni

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Pankaj, Chandre, Mahalle Parikshit, and Shinde Gitanjali. "Intrusion prevention system using convolutional neural network for wireless sensor network." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 504–15. https://doi.org/10.11591/ijai.v11.i2.pp504-515.

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Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep

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15

Sreenivasa Reddy, G., and G. Shyama Chandra Prasad. "INTRUSION DETECTION SYSTEM USING CLUSTERING ALGORITHMS OF NEURAL NETWORKS." International Journal of Advanced Research 11, no. 11 (2023): 607–14. http://dx.doi.org/10.21474/ijar01/17861.

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This research paper explores the application of clustering algorithms in neural networks for enhancing Intrusion Detection Systems (IDS). Intrusion Detection Systems are critical in safeguarding information systems from unauthorized access, misuse, or damage. The dynamic nature of cyber threats necessitates advanced approaches for detection and prevention. Neural networks, with their ability to learn and adapt, offer significant potential in identifying and classifying network intrusions. This paper reviews various neural network architectures and clustering algorithms, their integration in ID

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Bocu, Razvan, and Maksim Iavich. "Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks." Symmetry 15, no. 1 (2022): 110. http://dx.doi.org/10.3390/sym15010110.

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The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlin

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Jiang, Ya Ping, Shi Hui Cheng, and Yong Gan. "Network Security Prevention Model Based-Immune." Applied Mechanics and Materials 16-19 (October 2009): 881–85. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.881.

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With the concepts of self, nonself, antibody, vaccine and antigen in an intrusion detection and prevention system presented in this paper, the architecture of network intrusion and prevention based on immune principle is proposed. The intrusion information gotten from current monitored network is encapsulated and sent to the neighbor network as bacterin; therefore the neighbor network can make use of the bacterin and predict the danger of network. The experimental results show that the new model not only actualizes an active prevention method but also improves the ability of intrusion detectio

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Deineha, Taras, and Igor Svatovskiy. "Research of using the artificial intelligence algorithms in intrusion detection/prevention systems." Bulletin of V.N. Karazin Kharkiv National University, series «Mathematical modeling. Information technology. Automated control systems», no. 54 (June 27, 2022): 16–26. http://dx.doi.org/10.26565/2304-6201-2022-54-02.

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The analysis of the necessity and expediency of using artificial intelligence algorithms and technologies based on neural networks and fuzzy logic in systems for detecting and preventing network intrusions has been carried out. Modern network attacks are distinguished by the ability to change their characteristics and modes of action almost in real time. Outdated expert network protection systems based on the concept of "rule-action" can no longer cope with these types of attacks, because they need a certain time to process information about a new attack and store it into their database. The p

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Shankar, Akarshita, and Akshay Shankar. "Network Intrusion Detection and Prevention." International Journal of Applied Engineering Research 16, no. 4 (2021): 267. http://dx.doi.org/10.37622/ijaar/16.4.2021.267-270.

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Stiawan, Deris, Abdul Hanan Abdullah, and Mohd Yazid Idris. "Characterizing Network Intrusion Prevention System." International Journal of Computer Applications 14, no. 1 (2011): 11–18. http://dx.doi.org/10.5120/1811-2439.

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21

Erlansari, Aan, Funny Farady Coastera, and Afief Husamudin. "Early Intrusion Detection System (IDS) using Snort and Telegram approach." SISFORMA 7, no. 1 (2020): 21. http://dx.doi.org/10.24167/sisforma.v7i1.2629.

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Computer network security is an important factor that must be considered. Guaranteed security can avoid losses caused by attacks on the network security system. The most common prevention against network attacks is to place an administrator, but problems will arise when the administrator is not supervising the network, so to overcome these problems a system called IDS (Intrusion Detection System) can detect suspicious activity on the network through automating the work functions of an administrator. Snort is one of the software that functions to find out the intrusion. Data packets that pass t

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Sharma, Gaurav, and Anil Kumar Kapil. "INTRUSION DETECTION AND PREVENTION FRAMEWORK USING DATA MINING TECHNIQUES FOR FINANCIAL SECTOR." Acta Informatica Malaysia 5, no. 2 (2021): 58–61. http://dx.doi.org/10.26480/aim.02.2021.58.61.

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Security becomes the main concern when the resources are shared over a network for many purposes. For ease of use and time saving several services offered by banks and other financial companies are accessible over mobile apps and computers connected with the Internet. Intrusion detection (ID) is the act of detecting actions that attempt to compromise the confidentiality, integrity, or availability of a shared resource over a network. Intrusion detection does not include the prevention of intrusions. A different solution is required for intrusion prevention. The major intrusion detection techni

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Zhao, Wei Guo, and Gang Zhu Pan. "Application of Intrusion Prevention in Campus Network Security." Applied Mechanics and Materials 121-126 (October 2011): 4279–83. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4279.

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The safety menace of campus network is demonstrated and its network security is estimated in this paper, then combined with the development trend of future network, the necessity of deep inspection and intrusion prevention in campus network is analysized. Finally it puts forward and discusses thoroughly several representative strategies of disposing deep inspection and intrusion preventing in campus network.

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Veerasingam, Prakaash, Shukor Abd Razak, Ahmad Faisal Amri Abidin, Mohamad Afendee Mohamed, and Siti Dhalila Mohd Satar. "INTRUSION DETECTION AND PREVENTION SYSTEM IN SME'S LOCAL NETWORK BY USING SURICATA." Malaysian Journal of Computing and Applied Mathematics 6, no. 1 (2023): 21–30. http://dx.doi.org/10.37231/myjcam.2023.6.1.88.

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In the present era, Cybercriminals are increasingly focusing their attention on the local networks of SMEs. Due to the lack of resources and skilled workers in the cybersecurity field., SMEs struggle to prevent and detect fraudulent activities within their networks. To address this challenge, an Intrusion Detection and Prevention System (IDPS) is crucial for optimising network security in SMEs. This research paper explores the implementation of Suricata, an IDS/IPS tool, on a Raspberry Pi 2B embedded platform to create an effective IDPS for SMEs' the study demonstrates the viability of Suricat

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Kalaivani, A., and R. Pugazendi. "CNN-RBM Integrated Deep Learning Design for Categorizing Attack in an Intrusion Detection System." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 33, no. 05 (2025): 613–22. https://doi.org/10.1142/s0218488525400070.

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Currently, network attacks and intrusions are increasing due to expansions in computer networks. The most critical issue these days in modern cyber networks are network attacks. Intrusion prevention systems are designed to enhance security along with the firewalls and other intrusion prevention systems. Each and every network regardless of its size is exposed to network attacks. An Intrusion Detection System (IDS) is an essential security tool for categorizing malicious attacks in networks. Presently, Machine Learning (ML) and Deep Learning (DL) models are applied for developing a competent ID

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Onwodi, Greg. "Smart Intrusion Prevention System for the Local Area Network of Nigeria Immigration Special Study Centre, Gwagwalada, FCT, Abuja, Nigeria." Advances in Multidisciplinary and scientific Research Journal Publication 10, no. 4 (2022): 73–78. http://dx.doi.org/10.22624/aims/digital/v10n4p10.

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This study examines the Smart Intrusion Prevention System for the Local Area Network of Nigeria Immigration Special Study Centre, Gwagwalada, Abuja. Different research methodologies for Intrusion Prevention System was analysed and the hybrid based methodology was adopted because of its incorporation of the other methodologies. Configuration of the Intrusion Prevention System was studied and implemented using software known as Mikrotik. The Mikrotik software configuration was analysed properly. Among the major findings of the study was that the Local Area Network of Nigeria Immigration Special

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Subasree, S., Michael Jeniston S. Christ, M. Harish, V. Jeevaranjan, and S. Manikandan. "Enhanced Security through Intrusion Detection and Prevention System." Recent Trends in Cyber Criminology Research 1, no. 2 (2025): 1–5. https://doi.org/10.5281/zenodo.15582270.

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<em>This paper presents the design, implementation, and evaluation of a rule-based Intrusion Detection and Prevention System (IDPS) for network security. The proposed system employs signature-based detection, anomaly-based heuristics, and active prevention techniques to detect and mitigate various types of network intrusions with high efficiency. The system was developed to address the growing challenges in network security by providing a lightweight and effective approach that does not rely on machine learning algorithms. Our implementation utilizes Scapy for packet manipulation, custom rule

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Ugale, Archana R., and Amol D. Potgantwar. "Anomaly Based Intrusion Detection through Efficient Machine Learning Model." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 616–22. http://dx.doi.org/10.37391/ijeer.110251.

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Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets. Data and physical level security and analysis for Data protection have become more important as data volumes grow. IDSs collect and analyse data to identify s

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Mahesh T R, V Vivek, and Vinoth Kumar. "Implementation of Machine Learning-Based Data Mining Techniques for IDS." International Journal of Information Technology, Research and Applications 2, no. 1 (2023): 7–13. http://dx.doi.org/10.59461/ijitra.v2i1.23.

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The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract user

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Selva, Deepaa, Balakrishnan Nagaraj, Danil Pelusi, Rajendran Arunkumar, and Ajay Nair. "Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms." Algorithms 14, no. 8 (2021): 224. http://dx.doi.org/10.3390/a14080224.

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Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic alg

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Jeevaraj, Deepa, B. Karthik, T. Vijayan, and M. Sriram. "Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System." International journal of electrical and computer engineering systems 14, no. 2 (2023): 179–85. http://dx.doi.org/10.32985/ijeces.14.2.7.

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Intrusion detection models using machine-learning algorithms are used for Intrusion prediction and prevention purposes. Wireless sensor network has a possibility of being attacked by various kinds of threats that will de-promote the performance of any network. These WSN are also affected by the sensor networks that send wrong information because of some environmental causes in- built disturbances misaligned management of the sensors in creating intrusion to the wireless sensor networks. Even though signified routing protocols cannot assure the required security in wireless sensor networks. The

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Mahesh, T. R., Vivek V, and Kumar Vinoth. "Implementation of Machine Learning-Based Data Mining Techniques for IDS." International Journal of Information Technology, Research and Applications (IJITRA) ISSN: 2583 5343 2, no. 1 (2023): 7–13. https://doi.org/10.5281/zenodo.7780113.

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The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract

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Jumba Gin, AYUBA, SANUSI Mohammed, and JIMOH Abdurrahman Annivbassa. "Robust Design and Implementation of a Network IntrusionDetection System." International Journal of Scientific and Academic Research 03, no. 12 (2023): 01–07. http://dx.doi.org/10.54756/ijsar.2023.v3.12.1.

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A Network Intrusion Detection System (NIDS) is an essential component of network security that helps to identify and responds to potential security branches and attacks within a network infrastructure. Computer security has become a challenge and various tools and mechanisms have been developed in order to guarantee a safety level up to the requirements of modern life. Information security is of great concern these days due to the activities of hackers and malicious users on the Internet. Securing informationhas become a critical issue and is of growing concern as computer systems worldwide be

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Wahyudi, Farid, and Listanto Tri Utomo. "Perancangan Security Network Intrusion Prevention System Pada PDTI Universitas Islam Raden Rahmat Malang." Edumatic: Jurnal Pendidikan Informatika 5, no. 1 (2021): 60–69. http://dx.doi.org/10.29408/edumatic.v5i1.3278.

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Security is very main in computer networks, where many devices are connected to each other to interact and exchange data without limits. Network security is also a very important issue to prioritize, one of which is to use an intrusion prevention system. At PDTI UNIRA there are often network security problems, one of which is that during the test season, the server experiences many intrusion problems. The purpose of this research is to develop a social science system based on the analysis at PDTI of Raden Rahmat Islamic University Malang. This research method uses a computer security developme

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Tetskyi, Artem, та Artem Perepelitsyn. "Можливості використання апаратних прискорювачів у системах виявлення та запобігання вторгненням". Aerospace Technic and Technology, № 6 (21 листопада 2024): 94–102. https://doi.org/10.32620/aktt.2024.6.09.

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The subject of this study is the capabilities of FPGA technology for cybersecurity solutions with the network interface accelerators of SmartNIC, as well as the technologies for building, deploying, supporting, and accelerating intrusion detection systems and intrusion prevention systems. The goal of this work is to increase the performance of the network protection components of modern datacenters using hardware network interface accelerator cards based on FPGA technology. The task is to analyze the classification of cyber threats, to analyze methods of detecting cyber threats, to analyze the

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Li, Jie, and Yang Li. "Study of Application in Network Intrusion Prevention." Applied Mechanics and Materials 738-739 (March 2015): 1209–12. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.1209.

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Network intrusion prevention application are studied, analyzed vibration optical fiber, cable radiation, infrared radiation, tension fences, high-voltage pulse and other key technologies, proposed safe city, government investment, government rent, lease operator platform, operating mode, and applications in key areas of finance, police, parks and other surveillance industry.

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Tahir, Sabeen, Sheikh Tahir Bakhsh, and Rayan A. Alsemmeari. "An intrusion detection system for the prevention of an active sinkhole routing attack in Internet of things." International Journal of Distributed Sensor Networks 15, no. 11 (2019): 155014771988990. http://dx.doi.org/10.1177/1550147719889901.

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Internet of things (IoT) is a complex and massive wireless network, where millions of devices are connected together. These devices gather different types of data from different systems that transform human daily lives by modernizing home appliances, business, medicine, traveling, research, and so on. Security is a critical challenge for a stable IoT network, for instance, routing attacks, especially sinkhole attack is a severe attack which has the capability to direct network data toward the intruder, and it can also disrupt and disconnect the devices from their network. The IoT needs multi-f

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Safana Hyder Abbas, Wedad Abdul Khuder Naser, and Amal Abbas Kadhim. "Subject review: Intrusion Detection System (IDS) and Intrusion Prevention System (IPS)." Global Journal of Engineering and Technology Advances 14, no. 2 (2023): 155–58. http://dx.doi.org/10.30574/gjeta.2023.14.2.0031.

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Intrusion detection system (IDS) is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies. An intrusion prevention system (IPS) is software that has all the capabilities of an intrusion detection system and can also attempt to stop possible incidents. If anomaly traffic pass through the network IDS would generate a false positive which means it only detects the malicious traffic, takes no action and generates only alerts but IPS detect

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Safana, Hyder Abbas, Abdul Khuder Naser Wedad, and Abbas Kadhim Amal. "Subject review: Intrusion Detection System (IDS) and Intrusion Prevention System (IPS)." Global Journal of Engineering and Technology Advances 14, no. 2 (2023): 155–58. https://doi.org/10.5281/zenodo.7931783.

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Intrusion detection system (IDS) is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies. An intrusion prevention system (IPS) is software that has all the capabilities of an intrusion detection system and can also attempt to stop possible incidents. If anomaly traffic pass through the network IDS would generate  a false positive which means it only detects the malicious traffic, takes no action and generates only alerts but IPS

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Adi Wijaya, Adi Wijaya, Toibah Umi Kalsum, and Riska. "Penerapan OPNsense Sebagai Sistem Keamanan Web Server Menggunakan Metode Host Instrusion Prevention System." JURNAL AMPLIFIER : JURNAL ILMIAH BIDANG TEKNIK ELEKTRO DAN KOMPUTER 13, no. 2 (2023): 91–100. http://dx.doi.org/10.33369/jamplifier.v13i2.31514.

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This research was conducted to detect and prevent disturbances or intrusions that occur on web servers, because by default the security system on web servers in a network still depends on the administrator, so the security of server really depends on the alertness of an administrator in responding to disturbances that occur on the web server. This research is using experimental method. This research was carried out by implementing OPNsense as a web server security system using Host Intrusion Prevention System method. The experimental results are then documented to carry out analysis so that ap

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Siham, Aouad, El Meghrouni Issam, Sabri Yassine, Hilmani Adil, and Maizate Abderrahim. "Security of software defined networks: evolution and challenges." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 3 (2023): 384–91. https://doi.org/10.11591/ijres.v12.i3pp384-391.

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In software-defined networking (SDN), network traffic is managed by software controllers or application programming interfaces (APIs) rather than hardware components. It differs from traditional networks, which use switches and routers to control traffic. Using SDN, you can create and control virtual networks or traditional hardware networks. Furthermore, OpenFlow allows network administrators to control exact network behavior through centralized control of packet forwarding. For these reasons, SDN has advantages over certain security issues, unlike traditional networks. However, most of the e

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Yu, Tianxiang, Yang Xin, Hongliang Zhu, Qifeng Tang, and Yuling Chen. "Network Penetration Intrusion Prediction Based on Attention Seq2seq Model." Security and Communication Networks 2022 (May 4, 2022): 1–19. http://dx.doi.org/10.1155/2022/6012232.

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Intrusion detection is a critical component of network security. However, intrusion detection cannot play a very good role in the face of APT and 0 day. It needs to combine intrusion prevention, deception defense, and other technologies to ensure network security. Intrusion prediction is an important part of intrusion prevention and deception defense. Only by predicting the next possible attack can we prevent the corresponding intrusion or cheat adversary more efficiently. However, the current research on intrusion prediction has not received much attention. Most of the existing intrusion pred

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S, Bhaggiaraj, Shanthini S, Sugantha Mallika S.S., and Muthuram R. "NEXT-GENERATION INTRUSION DETECTION AND PREVENTION SYSTEMS FOR IT AND NETWORK SECURITY." ICTACT Journal on Communication Technology 14, no. 3 (2023): 2992–97. http://dx.doi.org/10.21917/ijct.2023.0445.

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In cybersecurity, the constant evolution of threats demands the development of next-generation Intrusion Detection and Prevention Systems (IDPS) to safeguard IT infrastructure and networks effectively. This research embarks on the journey of designing an innovative IDPS using a Dense VGG classifier, fueled by IoT data as its primary input source. Our approach combines the robustness of the Dense VGG architecture with the rich information generated by Internet of Things (IoT) devices, enhancing the system ability to detect and prevent intrusions. We gather diverse IoT data from sensors and devi

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Suryayusra, Suryayusra, and Dedi Irawan. "PERBANDINGAN INTRUSION PREVENTION SYSTEM (IPS) PADA LINUX UBUNTU DAN LINUX CENTOS." Jurnal Teknologi Informasi Mura 12, no. 02 (2020): 131–44. http://dx.doi.org/10.32767/jti.v12i02.1023.

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Perkembangan teknologi yang Semakin hari semakin meningkat, kita di tuntut untuk meningkatkan system keamanan jaringan yang kita miliki, karena semakin mudahnya orang bisa mengetahui tentang hacking dan cracking dan juga didukung oleh tools yang mudah didapatkan secara gratis. Dan untuk mencegah hal itu kita harus megamankan jaringan yang kita gunakan, untuk mengamankan jaringan tersebut peneliti menggunakan sebuah metode keamanan yang bernama Intrusion Prevention System (IPS), merupakan media yang banyak digunakan dalam membangun sebuah system keamanan komputer, lalu IPS di gabungkan dengan m

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Lin, Yi-Neng, Yao-Chung Chang, Ying-Dar Lin, and Yuan-Chen Lai. "Resource allocation in network processors for network intrusion prevention systems." Journal of Systems and Software 80, no. 7 (2007): 1030–36. http://dx.doi.org/10.1016/j.jss.2007.01.032.

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Ghawade, Miss Manoshri A. "Study of Intrusion Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 788–92. http://dx.doi.org/10.22214/ijraset.2021.34935.

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An intrusion detection system (IDS) could be a device or software application that observes a network for malicious activity or policy violations. Any malicious activity or violation is often reported or collected centrally employing a security information and event management system. Some IDS’s are proficient of responding to detected intrusion upon discovery. These are classified as intrusion prevention systems (IPS). A system that analyzes incoming network traffic is thought as Network intrusion detection system (NIDS). A system that monitors important software files is understood as Host i

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Guo, Guangfeng, Junxing Zhang, and Zhanfei Ma. "Intrusion prevention with attack traceback and software-defined control plane for campus networks." Computer Science and Information Systems, no. 00 (2020): 49. http://dx.doi.org/10.2298/csis200206049g.

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As traditional networks, the software-defined campus network also suffers from intrusion attacks. Current solutions for intrusion prevention cannot meet the requirements of the campus network. Existing methods of attack traceback are either limited to specific protocols or incur high overhead. To protect the data center (DC) of the campus network from internal and external attacks, we propose an Intrusion Prevention System (IPS) based on the coordinated control between the detection engine, the attack traceback agent, and the software-defined control plane. Our solution includes a novel algori

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Raniah Ali Mustafa and Haitham Salman Chyad. "Subject review: Cyber security using machine learning and deep learning techniques." Global Journal of Engineering and Technology Advances 16, no. 2 (2023): 212–19. http://dx.doi.org/10.30574/gjeta.2023.16.2.0161.

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In order to protection computers, programs, networks and data from intrusions and unauthorised access (UA), alteration, or demolition, a set of technologies and procedures is known as cybersecurity. A significant concern is the identification and prevention of a network intrusion. Methods like machine learning & deep learning identify network intrusions through estimating risk utilizing training data. Through the years, a number of machine learning & deep learning techniques have been introduced, and it has been demonstrated that these techniques are more accurate than other network in

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Raniah, Ali Mustafa, and Salman Chyad Haitham. "Subject review: Cyber security using machine learning and deep learning techniques." Global Journal of Engineering and Technology Advances 16, no. 2 (2023): 212–19. https://doi.org/10.5281/zenodo.10625682.

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In order to protection computers, programs, networks and data from intrusions and unauthorised access (UA), alteration, or demolition, a set of technologies and procedures is known as cybersecurity. A significant concern is the identification and prevention of a network intrusion. Methods like machine learning & deep learning identify network intrusions through estimating risk utilizing training data. Through the years, a number of machine learning & deep learning techniques have been introduced, and it has been demonstrated that these techniques are more accurate than other network in

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Samatha, Badugu, Thalakola Syamsundararao, and Nagarjuna Karyemsetty. "Deep Learning Based Intrusion Prevention System in Vehicular Network." Review of Computer Engineering Research 9, no. 3 (2022): 169–80. http://dx.doi.org/10.18488/76.v9i3.3145.

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In the internet of vehicles, safety-based communication is carried out for prevention, mitigation, and alleviation of accidents through cooperative messages, position sharing, and the exchange of speed data between the vehicle (nodes) and corresponding roadside units. However, such networks are susceptible to false alarms and mispositioning of vehicles. It is therefore imperative to authenticate and identify normal messages from aggressive and incorrect messages. In this context, this paper has emphasized on a deep learning technique utilizing binary classification for segregating normal and m

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