Relevant bibliographies by topics / Network intrusion detection system

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Network intrusion detection system'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Network intrusion detection system"

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Priyavengatesh, A. "A Predictive Model Using Deep Learning Neural Network for Efficient Intrusion Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 577–85. http://dx.doi.org/10.22214/ijraset.2023.56020.

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Abstract: Network intrusion detection system helps to detect exploitations and mitigate damages. A network intrusion detection system detects the network traffic that deviates from the normal behavioral pattern. Developing an efficient intrusion detection system has many challenges and the patterns associated with one type of intrusion differ from other intrusions. In such situations, understanding different patterns and differentiating intrusions becomes essential to detect anomalies and attacks in the network. Deep learning models offer more power and intelligence to the detection system and
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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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Solomon, Irin Anna, Aman Jatain, and Shalini Bhaskar Bajaj. "Intrusion Detection System Using Deep Learning." Asian Journal of Computer Science and Technology 8, no. 2 (2019): 105–10. http://dx.doi.org/10.51983/ajcst-2019.8.2.2132.

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Intrusion detection system (IDS) plays a very critical part in identifying threats and monitoring malicious activities in networking system. The system administrators can use IDS to detect unauthorized access by intruders in different organizations. It has become an inevitable element to the security administration of every organization. IDSs can be generally categorized into two categories. The first group focuses on patterns/signatures of network packets/traffic and they identify network intrusions using rule-based matching. The second group uses machine learning (ML) based approaches such a
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Ali, Rashid, and Supriya Kamthania. "A Comparative Study of Different Relevant Features Hybrid Neural Networks Based Intrusion Detection Systems." Advanced Materials Research 403-408 (November 2011): 4703–10. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4703.

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Intrusion detection is the task of detecting, preventing and possibly reacting to the attacks and intrusions in a network based computer system. The neural network algorithms are popular for their ability to ’learn’ the so called patterns in a given environment. This feature can be used for intrusion detection, where the neural network can be trained to detect intrusions by recognizing patterns of an intrusion. In this work, we propose and compare the three different Relevant Features Hybrid Neural Networks based intrusion detection systems, where in, we first recognize the most relevant featu
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Abdulhameed, Abbas A., Sundos A. Hameed Alazawi, and Ghassan Muslim Hassan. "An optimized model for network intrusion detection in the network operating system environment." Mesopotamian Journal of CyberSecurity 4, no. 3 (2024): 75–85. http://dx.doi.org/10.58496/mjcs/2024/017.

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With the heavy reliance on computers and information technology to send and receive data across networks of various types, there has been concern about securing that data from intrusions and cyber-attacks. The expansion of network usage has led to an increase in hacker attacks, which has led to prioritizing cybersecurity precautions in detecting potential threats. Intrusion detection techniques are a critical security measure to protect networks in both personal and corporate environments that are managed by network operating systems. For this, the paper relies on designing a network intrusion
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Veselý, A., and D. Brechlerová. "Neural networks in intrusion detection systems." Agricultural Economics (Zemědělská ekonomika) 50, No. 1 (2012): 35–40. http://dx.doi.org/10.17221/5164-agricecon.

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Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose, Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to the known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching for an abnormal system activity. Most IDS commercial
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A P, Niharika. "Deep Learning Approach for Intrusion Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33646.

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The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is tool that helps to detect intrusions by inspecting the network traffic. A system called an intrusion detection system (IDS) observes network traffic for malicious transactions and sends immediate alerts
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Qazi, Emad Ul Haq, Muhammad Hamza Faheem, and Tanveer Zia. "HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System." Applied Sciences 13, no. 8 (2023): 4921. http://dx.doi.org/10.3390/app13084921.

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Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building a
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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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Ahmad, Iftikhar, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi, and Rayed A. AlGhamdi. "An Efficient Network Intrusion Detection and Classification System." Mathematics 10, no. 3 (2022): 530. http://dx.doi.org/10.3390/math10030530.

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Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. Th
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Dissertations / Theses on the topic "Network intrusion detection system"

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Maharjan, Nadim, and Paria Moazzemi. "Telemetry Network Intrusion Detection System." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581632.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California<br>Telemetry systems are migrating from links to networks. Security solutions that simply encrypt radio links no longer protect the network of Test Articles or the networks that support them. The use of network telemetry is dramatically expanding and new risks and vulnerabilities are challenging issues for telemetry networks. Most of these vulnerabilities are silent in nature and c
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Moten, Daryl, and Farhad Moazzami. "Telemetry Network Intrusion Detection Test Bed." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579527.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV<br>The transition of telemetry from link-based to network-based architectures opens these systems to new security risks. Tools such as intrusion detection systems and vulnerability scanners will be required for emerging telemetry networks. Intrusion detection systems protect networks against attacks that occur once the network boundary has been breached. An intrusion detection model was developed in
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Langin, Chester Louis. "A SOM+ Diagnostic System for Network Intrusion Detection." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/389.

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This research created a new theoretical Soft Computing (SC) hybridized network intrusion detection diagnostic system including complex hybridization of a 3D full color Self-Organizing Map (SOM), Artificial Immune System Danger Theory (AISDT), and a Fuzzy Inference System (FIS). This SOM+ diagnostic archetype includes newly defined intrusion types to facilitate diagnostic analysis, a descriptive computational model, and an Invisible Mobile Network Bridge (IMNB) to collect data, while maintaining compatibility with traditional packet analysis. This system is modular, multitaskable, scalable, i
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Ademi, Muhamet. "Web-Based Intrusion Detection System." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20271.

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Web applications are growing rapidly and as the amount of web sites globallyincreases so do security threats. Complex applications often interact with thirdparty services and databases to fetch information and often interactions requireuser input. Intruders are targeting web applications specifically and they are ahuge security threat to organizations and a way to combat this is to haveintrusion detection systems. Most common web attack methods are wellresearched and documented however due to time constraints developers oftenwrite applications fast and may not implement the best security pract
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Fleming, Theodor, and Hjalmar Wilander. "Network Intrusion and Detection : An evaluation of SNORT." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-144335.

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Network security has become a vital part for computer networks to ensure that they operate as expected. With many of today's services relying on networks it is of great importance that the usage of networks are not being compromised. One way to increase the security of a computer network is to implement a Network Intrusion Detection System (NIDS). This system monitors the traffic sent to, from and within the network. This study investigates how a NIDS called SNORT with different configurations handles common network attacks. The knowledge of how SNORT managed the attacks is used to evaluate an
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Hashmi, Adeel. "Hardware Acceleration of Network Intrusion Detection System Using FPGA." Thesis, Manchester Metropolitan University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.526973.

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Stanley, Fred Philip. "Intrusion detection and response for system and network attacks." [Ames, Iowa : Iowa State University], 2009.

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Caulkins, Bruce. "SESSION-BASED INTRUSION DETECTION SYSTEM TO MAP ANOMALOUS NETWORK TRAFFIC." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3466.

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Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal &#150; firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other
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Goh, Vik Tor. "Intrusion detection framework for encrypted networks." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41733/1/Vik_Tor_Goh_Thesis.pdf.

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Network-based Intrusion Detection Systems (NIDSs) monitor network traffic for signs of malicious activities that have the potential to disrupt entire network infrastructures and services. NIDS can only operate when the network traffic is available and can be extracted for analysis. However, with the growing use of encrypted networks such as Virtual Private Networks (VPNs) that encrypt and conceal network traffic, a traditional NIDS can no longer access network traffic for analysis. The goal of this research is to address this problem by proposing a detection framework that allows a commercial
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Karkera, Akhil Narayan. "Design and implementation of a policy-based intrusion detection system generic intrusion detection model for a distributed network /." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000550.

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Books on the topic "Network intrusion detection system"

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Ning, Peng. Intrusion Detection in Distributed Systems: An Abstraction-Based Approach. Springer US, 2004.

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Stephen, Northcutt, and Edmead Mark T, eds. Inside network perimeter security: The definitive guide to firewalls, VPNs, routers, and intrusion detection systems. New Riders, 2003.

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Judy, Novak, ed. Network intrusion detection. 3rd ed. New Riders Pub., 2002.

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Ghorbani, Ali A., Wei Lu, and Mahbod Tavallaee. Network Intrusion Detection and Prevention. Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-88771-5.

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Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. Network Intrusion Detection using Deep Learning. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1444-5.

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Marchette, David J. Computer Intrusion Detection and Network Monitoring. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3458-4.

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Judy, Novak, and McLachlan Donald, eds. Network intrusion detection: An analyst's handbook. 2nd ed. New Riders, 2001.

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Manu, Zacharia, ed. Network intrusion alert: An ethical hacking guide to intrusion detection. Thomson Course Technology PTR, 2008.

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Escamilla, Terry. Intrusion detection: Network security beyond the firewall. John Wiley, 1998.

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1980-, Lu Wei, and Tavallaee Mahbod, eds. Network intrusion detection and prevention: Concepts and techniques. Springer, 2010.

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Book chapters on the topic "Network intrusion detection system"

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Griffith, SueAnne, and Thomas H. Morris. "Network Intrusion Detection System." In Encyclopedia of Cryptography, Security and Privacy. Springer Berlin Heidelberg, 2021. https://doi.org/10.1007/978-3-642-27739-9_1491-1.

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Griffith, SueAnne, and Thomas H. Morris. "Network Intrusion Detection System." In Encyclopedia of Cryptography, Security and Privacy. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-030-71522-9_1491.

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Rosenberg, Ishai, and Ehud Gudes. "Evading System-Calls Based Intrusion Detection Systems." In Network and System Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46298-1_14.

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Kizza, Joseph Migga. "System Intrusion Detection and Prevention." In Guide to Computer Network Security. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6654-2_13.

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Kizza, Joseph Migga. "System Intrusion Detection and Prevention." In Guide to Computer Network Security. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4543-1_13.

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Kizza, Joseph Migga. "System Intrusion Detection and Prevention." In Guide to Computer Network Security. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55606-2_13.

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Cho, Hyeyoung, Daeyoung Kim, Juhong Kim, Yoonmee Doh, and Jongsoo Jang. "Network Processor Based Network Intrusion Detection System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25978-7_98.

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Deshpande, Prachi S., Subhash C. Sharma, and Sateesh K. Peddoju. "A Network-Based Intrusion Detection System." In Studies in Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6089-3_3.

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Park, Woo Young, Sang Hyun Kim, Duy-Son Vu, Chang Han Song, Hee Soo Jung, and Hyeon Jo. "Intrusion Detection System for Industrial Network." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16075-2_48.

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Qassim, Qais Saif, Norziana Jamil, and Mohammed Najah Mahdi. "Symptoms-Based Network Intrusion Detection System." In Advances in Visual Informatics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90235-3_42.

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Conference papers on the topic "Network intrusion detection system"

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Beg, Mirza Ibrahim, and Mohd Yousuf Ansari. "Network Intrusion Detection System using Autoencoders." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10931842.

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Parekh, Nishank, Arzob Sen, P. Rajasekaran, J. D. Dorathi Jayaseeli, and P. Robert. "Network Intrusion Detection System Using Optuna." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823298.

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Oroian, David, Roland Bolboaca, Adrian-Silviu Roman, and Virgil Dobrota. "Network Intrusion Detection System Using Anomaly Detection Techniques." In 2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2024. https://doi.org/10.1109/iccp63557.2024.10793023.

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Setiawan, Andrih, Agung Mulyo Widodo, Gerry Firmansyah, Nenden Siti Fatonah, Budi Tjahjono, and Andika Wisnujati. "Network Intrusion Detection Using 1D Convolutional Neural Networks." In 2024 4th International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS). IEEE, 2024. https://doi.org/10.1109/ice3is62977.2024.10775512.

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Garg, Ishaan, Priyansh Sharma, Gurveer Singh, Purushottam Sharma, and Vansh Sharma. "Network Intrusion Detection System: Machine Learning Approach." In 2024 Second International Conference on Advanced Computing & Communication Technologies (ICACCTech). IEEE, 2024. https://doi.org/10.1109/icacctech65084.2024.00045.

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Monteiro, João, and Bruno Sousa. "eBPF Intrusion Detection System with XDP Offload support." In 2024 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2024. https://doi.org/10.1109/nfv-sdn61811.2024.10807487.

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Patil, Harshal, Yogesh Lohumi, K. M. Tejaswini, H. C. Sudheendramouli, Swapnil Parikh, and Ramya Maranan. "Distributed Machine Learning System for Intrusion Detection." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691032.

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Silva Neto, Manuel Gonçalves da, and Danielo G. Gomes. "Network Intrusion Detection Systems Design: A Machine Learning Approach." In XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbrc.2019.7413.

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With the increasing popularization of computer network-based technologies, security has become a daily concern, and intrusion detection systems (IDS) play an essential role in the supervision of computer networks. An employed approach to combat network intrusions is the development of intrusion detection systems via machine learning techniques. The intrusion detection performance of these systems depends highly on the quality of the IDS dataset used in their design and the decision making for the most suitable machine learning algorithm becomes a difficult task. The proposed paper focuses on e
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Prasad, Romesh, and Young Moon. "Adaptive Intrusion Detection System for Cyber-Manufacturing System." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70017.

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Abstract While Cyber-Manufacturing System security must involve three separate yet interrelated processes (prediction, detection, and prevention), the detection process is the focus of research presented in this paper. Current intrusion detection systems often result in high false positive and false negative rates. Also, the actual detection time may take long time-up to several months. The current intrusion detection systems rely heavily on the network data, but do not utilize the physical data such as side channel, sensor reading, image, keystrokes., which are generated during manufacturing
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S. P, Sujini, AnbuShamini G. N, and Prija J. S. "Deep Intrusion Detection for DOS and DDOS Attacks Using LSTM and Deep Autoencoder Neural Network." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/qkfn6548/ngcesi23p93.

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Early detection of network intrusions is a very important factor in network security. However, most studies of network intrusion detection systems utilize features for full sessions, making it difficult to detect intrusions before a session ends. To solve this problem, the proposed method uses packet data for features to determine if packets are malicious traffic. Such an approach inevitably increases the probability of falsely detecting normal packets as an intrusion or an intrusion as normal traffic for the initial session. As a solution, the proposed method learns the patterns of packets th
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Reports on the topic "Network intrusion detection system"

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Zage, Dolores M., and Wayne M. Zage. Intrusion Detection System Visualization of Network Alerts. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532723.

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Heady, R., G. Luger, A. Maccabe, and M. Servilla. The architecture of a network level intrusion detection system. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/425295.

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Chen, Yan. HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1108982.

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Heady, R., G. F. Luger, A. B. Maccabe, M. Servilla, and J. Sturtevant. A prototype implementation of a network-level intrusion detection system. Technical report number CS91-11. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/425286.

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Kemmer, Richard A., and Giovanni Vigna. A Model-Based Real-Time Intrusion Detection System for Large Scale Heterogeneous Networks. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada420824.

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f
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McLain, C. D., A. Studer, and R. P. Lippmann. Making Network Intrusion Detection Work With IPsec. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada468587.

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Ye, Nong. Computer Network Equipment for Intrusion Detection Research. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada381649.

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Lundy, Philip A., George W. Pittman, and Heinz J. Pletsch. Intrusion Detection System Methodology Investigation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198210.

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Grosskopf, Michael John. Aligning Time Series for Cyber-Physical Network Intrusion Detection. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1212612.

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