Relevant bibliographies by topics / Intrusion detection systems (Computer security)

Contents

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

Academic literature on the topic 'Intrusion detection systems (Computer security)'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 July 2024

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Journal articles on the topic "Intrusion detection systems (Computer security)"

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Mane, Prof Dipali. "Machine Learning Algorithms for Intrusion Detection in Cybersecurity." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6315–21. http://dx.doi.org/10.22214/ijraset.2023.52788.

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Abstract: Computer networks and virtual machine security are very necessary in today’s time. An Intrusion Detection System (IDS) is a security mechanism designed to monitor computer networks or systems for malicious activities or unauthorized access attempts. The primary function of an IDS is to detect and respond to potential security breaches in real time. Tasks performed by an IDS are anomaly detection, Signature detection, security alert generation, etc… Various researchers are actively working on different ideas for increasing the performance of the IDS. We have used a machine-learning ap
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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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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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Battini Sujatha, Et al. "An Efficient Fuzzy Based Multi Level Clustering Model Using Artificial Bee Colony For Intrusion Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 264–73. http://dx.doi.org/10.17762/ijritcc.v11i11.9390.

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Network security is becoming increasingly important as computer technology advances. One of the most important components in maintaining a secure network is an Intrusion Detection System (IDS). An IDS is a collection of tools used to detect and report network anomalies. Threats to computer networks are increasing at an alarming rate. As a result, it is critical to create and maintain a safe computing environment. For network security, researchers employ a range of technologies, including anomaly-based intrusion detection systems (AIDS). These anomaly-based detections face a major challenge in
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Sun, Yu Tao. "Design and Research on Intrusion Detection System in the Computer Network Security." Applied Mechanics and Materials 416-417 (September 2013): 1418–22. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1418.

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This paper first discusses the information security and network security, security threat, hacker intrusion process, system and network security vulnerabilities, and then introduces the status of intrusion detection system. By the comparison of two kinds of intrusion detection systems, the article puts forward the detection system based on the combination of the soil and the intrusion of network intrusion detection technology. Combined with the actual project development, this article focuses on the key technology design idea and the realization of the intrusion detection system in network sec
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Ayachi, Yassine, Youssef Mellah, Mohammed Saber, Noureddine Rahmoun, Imane Kerrakchou, and Toumi Bouchentouf. "A survey and analysis of intrusion detection models based on information security and object technology-cloud intrusion dataset." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1607. http://dx.doi.org/10.11591/ijai.v11.i4.pp1607-1614.

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Nowadays society, economy, and critical infrastructures have become principally dependent on computers, networks, and information technology solutions, on the other side, cyber-attacks are becoming more sophisticated and thus presenting increasing challenges in accurately detecting intrusions. Failure to prevent intrusions could compromise data integrity, confidentiality, and availability. Different detection methods are proposed to tackle computer security threats, which can be broadly classified into anomaly-based intrusion detection systems (AIDS) and signature-based intrusion detection sys
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Lafta, Hameed. "Network Intrusion Detection Using Optimal Perception with Cuckoo Algorithm." Wasit Journal for Pure sciences 3, no. 1 (2024): 95–105. http://dx.doi.org/10.31185/wjps.326.

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ABSTRACT To safeguard computer networks from intruders, intrusion detection systems have been created. These systems operate in conjunction with firewalls and other security measures to guarantee the safety and efficiency of the computer system. An intrusion detection system is a tool designed to detect and pinpoint attacks and vulnerabilities within a network or computer system. It subsequently notifies the system administrator of them. The primary challenge with intrusion detection systems is enhancing their speed and precision in detecting intruders. This article explores a novel technique
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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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P, Wanda. "A Survey of Intrusion Detection System." International Journal of Informatics and Computation 1, no. 1 (2020): 1. http://dx.doi.org/10.35842/ijicom.v1i1.7.

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Nowadays, the evolution of the internet and the use of computer systems has resulted in the huge electronic transformation of data that experienced multiple problems such as security, privacy, and confidentiality of information. Significant progress has been made in terms of improving computer systems security. However, security, privacy, and confidentiality of electronic systems are potentially major problems in computer systems. In this paper, we presented a survey on intrusion detection systems (IDS) in several areas. It consists of Web Application, Cloud Environment, Internet of Things (Io
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Dissertations / Theses on the topic "Intrusion detection systems (Computer security)"

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Lydon, Andrew. "Compilation For Intrusion Detection Systems." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1088179093.

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Asarcıklı, Şükran Tuğlular Tuğkan. "Firewall monitoring using intrusion detection systems/." [s.l.]: [s.n.], 2005. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000390.pdf.

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Jacoby, Grant A. "Battery-based intrusion detection /." This resource online, 2005. http://scholar.lib.vt.edu/theses/available/etd-04212005-120840.

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Yüksel, Ulaş Tuğlular Tuğkan. "Development of a Quality Assurance Prototype for Intrusion Detection Systems/." [s.l.]: [s.n.], 2002. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000131.pdf.

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Fogla, Prahlad. "Improving the Efficiency and Robustness of Intrusion Detection Systems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19772.

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With the increase in the complexity of computer systems, existing security measures are not enough to prevent attacks. Intrusion detection systems have become an integral part of computer security to detect attempted intrusions. Intrusion detection systems need to be fast in order to detect intrusions in real time. Furthermore, intrusion detection systems need to be robust against the attacks which are disguised to evade them. We improve the runtime complexity and space requirements of a host-based anomaly detection system that uses q-gram matching. q-gram matching is often used for approxima
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Botes, Frans Hendrik. "Ant tree miner amyntas for intrusion detection." Thesis, Cape Peninsula University of Technology, 2018. http://hdl.handle.net/20.500.11838/2865.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2018.<br>With the constant evolution of information systems, companies have to acclimatise to the vast increase of data flowing through their networks. Business processes rely heavily on information technology and operate within a framework of little to no space for interruptions. Cyber attacks aimed at interrupting business operations, false intrusion detections and leaked information burden companies with large monetary and reputational costs. Intrusion detection systems analyse network traffic to identify susp
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Arvidson, Martin, and Markus Carlbark. "Intrusion Detection Systems : Technologies, Weaknesses and Trends." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1614.

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<p>Traditionally, firewalls and access control have been the most important components used in order to secure servers, hosts and computer networks. Today, intrusion detection systems (IDSs) are gaining attention and the usage of these systems is increasing. This thesis covers commercial IDSs and the future direction of these systems. A model and taxonomy for IDSs and the technologies behind intrusion detection is presented. </p><p>Today, many problems exist that cripple the usage of intrusion detection systems. The decreasing confidence in the alerts generated by IDSs is directly related to s
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Gupta, Kapil Kumar. "Robust and efficient intrusion detection systems." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/3588.

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Intrusion Detection systems are now an essential component in the overall network and data security arsenal. With the rapid advancement in the network technologies including higher bandwidths and ease of connectivity of wireless and mobile devices, the focus of intrusion detection has shifted from simple signature matching approaches to detecting attacks based on analyzing contextual information which may be specific to individual networks and applications. As a result, anomaly and hybrid intrusion detection approaches have gained significance. However, present anomaly and hybrid detection app
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Uzuncaova, Engin. "A generic software architecture for deception-based intrusion detection and response systems." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FUzuncaova.pdf.

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Thesis (M.S. in Computer Science and M.S. in Software Engineering)--Naval Postgraduate School, March 2003.<br>Thesis advisor(s): James Bret Michael, Richard Riehle. Includes bibliographical references (p. 63-66). Also available online.
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Kalibjian, Jeffrey R. "APPLICATION OF INTRUSION DETECTION SOFTWARE TO PROTECT TELEMETRY DATA IN OPEN NETWORKED COMPUTER ENVIRONMENTS." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/606817.

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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California<br>Over the past few years models for Internet based sharing and selling of telemetry data have been presented [1] [2] [3] at ITC conferences. A key element of these sharing/selling architectures was security. This element was needed to insure that information was not compromised while in transit or to insure particular parties had a legitimate right to access the telemetry data. While the software managing the telemetry data needs to be security consc
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Books on the topic "Intrusion detection systems (Computer security)"

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Roberto, Di Pietro, and SpringerLink (Online service), eds. Intrusion Detection Systems. Springer-Verlag US, 2008.

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Beale, Jay. Snort 2.0 intrusion detection. Syngress, 2003.

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C, Foster James, ed. Snort 2.0 intrusion detection. Syngress, 2003.

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Real world Linux security: Intrusion protection, detection, and recovery. 2nd ed. Prentice Hall, 2003.

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Toxen, Bob. Real-world Linux security: Intrusion, prevention, detection, and recovery. Prentice Hall, 2001.

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Sushil, Jajodia, and Wang Sean 1960-, eds. Intrusion detection in distributed systems: An abstraction-based approach. Kluwer Academic Publishers, 2004.

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Real-world Linux security: Intrusion, prevention, detection, and recovery. Prentice Hall, 2001.

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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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National Institute of Standards and Technology (U.S.), ed. An overview of issues in testing intrusion detection systems. U.S. Dept. of Commerce, National Institute of Standards and Technology, 2003.

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

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Book chapters on the topic "Intrusion detection systems (Computer security)"

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Cho, Sung-Bae, and Sang-Jun Han. "Intrusion Detection for Computer Security." In Computationally Intelligent Hybrid Systems. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9780471683407.ch8.

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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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Gu, Guofei, Prahlad Fogla, David Dagon, Wenke Lee, and Boris Skoric. "Towards an Information-Theoretic Framework for Analyzing Intrusion Detection Systems." In Computer Security – ESORICS 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11863908_32.

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Lee, Sin Yeung, Wai Lup Low, and Pei Yuen Wong. "Learning Fingerprints for a Database Intrusion Detection System." In Computer Security — ESORICS 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45853-0_16.

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Hamed, Tarfa, Jason B. Ernst, and Stefan C. Kremer. "A Survey and Taxonomy of Classifiers of Intrusion Detection Systems." In Computer and Network Security Essentials. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58424-9_2.

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Gu, Jie. "An Effective Intrusion Detection Model Based on Pls-Logistic Regression with Feature Augmentation." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_10.

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AbstractComputer network is playing a significantly important role in our society, including commerce, communication, consumption and entertainment. Therefore, network security has become increasingly important. Intrusion detection systems have received considerable attention, which not only can detect known attacks or intrusions, but also can detect unknown attacks. Among the various methods applied to intrusion detection, logistic regression is the most widely used, which can achieve good performances and have good interpretability at the same time. However, intrusion detection systems usually confront with data of large scale and high dimension. How to reduce the dimension and improve the data quality is significant to improve the detection performances. Therefore, in this paper, we propose an effective intrusion detection model based on pls-logistic regression with feature augmentation. More specifically, the feature augmentation technique is implemented on the original features with goal of obtaining high-qualified training data; and then, pls-logistic regression is applied on the newly transformed data to perform dimension reduction and detection model building. The NSL-KDD dataset is used to evaluate the proposed method, and the empirical results show that our proposed method can achieve good performances in terms of accuracy, detection rate and false alarm rate.
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Ernst, Jason, Tarfa Hamed, and Stefan Kremer. "A Survey and Comparison of Performance Evaluation in Intrusion Detection Systems." In Computer and Network Security Essentials. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58424-9_32.

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Amato, Flora, Giovanni Cozzolino, Antonino Mazzeo, and Emilio Vivenzio. "Using Multilayer Perceptron in Computer Security to Improve Intrusion Detection." In Intelligent Interactive Multimedia Systems and Services 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59480-4_22.

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Conference papers on the topic "Intrusion detection systems (Computer security)"

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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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Grzech, Adam, and Mariusz Kazmierski. "Distributed Intrusion Detection Systems of Computer Communication Networks." In 2008 New Technologies, Mobility and Security (NTMS). IEEE, 2008. http://dx.doi.org/10.1109/ntms.2008.ecp.39.

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Besharatloo, Mohammad, Atiye Rahimizadeh, and Masoud Besharatloo. "Hybrid Intrusion Detection Model for Computer Networks." In 11th International Conference on Signal Image Processing and Multimedia. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130906.

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redunda
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Massicotte, Frederic, Francois Gagnon, Yvan Labiche, Lionel Briand, and Mathieu Couture. "Automatic Evaluation of Intrusion Detection Systems." In 2006 22nd Annual Computer Security Applications Conference (ACSAC'06). IEEE, 2006. http://dx.doi.org/10.1109/acsac.2006.15.

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Elaeraj, Ouafae, and Cherkaoui Leghris. "The Evolution of Vector Machine Support in the Field of Intrusion Detection Systems." In 2nd International Conference on Machine Learning Techniques and Data Science (MLDS 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111817.

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With the increase in Internet and local area network usage, malicious attacks and intrusions into computer systems are growing. The design and implementation of intrusion detection systems became extremely important to help maintain good network security. Support vector machines (SVM), a classic pattern recognition tool, has been widely used in intrusion detection. They make it possible to process very large data with great efficiency and are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of
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Awad, Mohamed TajElsir, Sally Mohamed Aldaw, Salma Mohamed Aldaw, and Babekir A. rahman Osman. "Video Security System for Intrusion Detection." In 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2019. http://dx.doi.org/10.1109/iccceee46830.2019.9070823.

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Wolsing, Konrad, Eric Wagner, and Martin Henze. "Facilitating Protocol-independent Industrial Intrusion Detection Systems." In CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2020. http://dx.doi.org/10.1145/3372297.3420019.

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J, Latha, KarthickMurugan S, and Logalakshmi A. "Computer Networks Cyber Security Via an Intrusion Detection System." In 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2023. http://dx.doi.org/10.1109/rmkmate59243.2023.10369517.

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Yampolskiy, Roman V. "Indirect Human Computer Interaction-Based Biometrics for Intrusion Detection Systems." In 2007 41st Annual IEEE International Carnahan Conference on Security Technology. IEEE, 2007. http://dx.doi.org/10.1109/ccst.2007.4373481.

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El-Kosairy, Ahmed, and Marianne A. Azer. "Intrusion and ransomware detection system." In 2018 1st International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2018. http://dx.doi.org/10.1109/cais.2018.8471688.

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Reports on the topic "Intrusion detection systems (Computer security)"

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Ingram, Dennis J., H. S. Kremer, and Neil C. Rowe. Distributed Intrusion Detection for Computer Systems Using Communicating Agents. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada458055.

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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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