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

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  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 approach for intrusion detection. We have used SVM, Random Forests, and Decision trees for detecting intrusions.
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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 Prevention and Detection which facilitate to avoid suspicious users. Deep learning have been widely used in recent years to improve intrusion detection in networks. These techniques allow the automatic detection of network traffic anomalies. This paper presents literature review on intrusion detection techniques.
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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 solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.
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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 solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.
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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 the classification of data. Optimization algorithms that mimic the foraging behavior of bees in nature, such as the artificial bee colony algorithm, is a highly successful tool. A computer network's intrusion detection system (IDS) is an essential tool for keeping tabs on the activities taking place in the network. Artificial Bee Colony (ABC) algorithm is used in this research for effective intrusion detection. More and more intrusion detection systems are needed to keep up with the increasing number of attacks and the increase in Internet bandwidth. Detecting developing threats with high accuracy at line rates is the prerequisite for a good intrusion detection system. As traffic grows, current systems will be overwhelmed by the sheer volume of false positives and negatives they generate. In order to detect intrusions based on anomalies, this research employs an Efficient Fuzzy based Multi Level Clustering Model using Artificial Bee Colony (EFMLC-ABC). A semi-supervised intrusion detection method based on an artificial bee colony algorithm is proposed in this paper to optimize cluster centers and identify the best clustering options. In order to assess the effectiveness of the proposed method, various subsets of the KDD Cup 99 database were subjected to experimental testing. Analyses have shown that the proposed algorithm is suitable and efficient for intrusion detection system.
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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 security.
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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 systems (SIDS). One of the most preferred AIDS mechanisms is the machine learning-based approach which provides the most relevant results ever, but it still suffers from disadvantages like unrepresentative dataset, indeed, most of them were collected during a limited period of time, in some specific networks and mostly don't contain up-to-date data. Additionally, they are imbalanced and do not hold sufficient data for all types of attacks, especially new attack types. For this reason, upto-date datasets such as information security and object technology-cloud intrusion dataset (ISOT-CID) are very convenient to train predictive models on a cloud-based intrusion detection approach. The dataset has been collected over a sufficiently long period and involves several hours of attack data, culminating into a few terabytes. It is large and diverse enough to accommodate machine-learning studies.
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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 for identifying attempts to infiltrate computer systems. The system utilizes a hybrid approach involving the cuckoo algorithm and perceptron neural network. This novel approach can detect intrusion data more accurately than previous methods and enhance the detection rate by over 1%. The system utilizes the cuckoo method to choose a subset of characteristics, which are then analyzed based on the frequency of various attribute types in intrusive and normal data using an optimum perceptron. The system has been evaluated and the implementation has yielded a detection accuracy of 89.8%, representing a substantial enhancement compared to earlier approaches.
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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 tools are misuse systems with rule-based expert system structure. However, these techniques are less successful when attack characteristics vary from built-in signatures. Artificial neural networks offer the potential to resolve these problems. As far as anomaly systems are concerned, it is very difficult to build them, because it is difficult to define the normal and abnormal behaviour of a system. Also for building anomaly system, neural networks can be used, because they can learn to discriminate the normal and abnormal behaviour of a system from examples. Therefore, they offer a promising technique for building anomaly systems. This paper presents an overview of the applicability of neural networks in building intrusion systems and discusses advantages and drawbacks of neural network technology.
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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 (IoT), Mobile Ad-Hoc Network (MANET), Wireless Sensor Network (WSN) and Voice over Internet Protocol (VOIP)
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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 approximate substring matching problems in a wide range of application areas, including intrusion detection. During the text pre-processing phase, we store all the q-grams present in the text in a tree. We use a tree redundancy pruning algorithm to reduce the size of the tree without losing any information. We also use suffix links for fast linear-time q-gram search during query matching. We compare our work with the Rabin-Karp based hash-table technique, commonly used for multiple q-gram matching. To analyze the robustness of network anomaly detection systems, we develop a new class of polymorphic attacks called polymorphic blending attacks, that can effectively evade payload-based network anomaly IDSs by carefully matching the statistics of the mutated attack instances to the normal profile. Using PAYL anomaly detection system for our case study, we show that these attacks are practically feasible. We develop a formal framework which is used to analyze polymorphic blending attacks for several network anomaly detection systems. We show that generating an optimal polymorphic blending attack is NP-hard for these anomaly detection systems. However, we can generate polymorphic blending attacks using the proposed approximation algorithms. The framework can also be used to improve the robustness of an intrusion detector. We suggest some possible countermeasures one can take to improve the robustness of an intrusion detection system against polymorphic blending attacks.
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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 suspicious patterns that intent to compromise the system. Classifiers (algorithms) are used to classify the data within different categories e.g. malicious or normal network traffic. Recent surveys within intrusion detection highlight the need for improved detection techniques and warrant further experimentation for improvement. This experimental research project focuses on implementing swarm intelligence techniques within the intrusion detection domain. The Ant Tree Miner algorithm induces decision trees by using ant colony optimisation techniques. The Ant Tree Miner poses high accuracy with efficient results. However, limited research has been performed on this classifier in other domains such as intrusion detection. The research provides the intrusion detection domain with a new algorithm that improves upon results of decision trees and ant colony optimisation techniques when applied to the domain. The research has led to valuable insights into the Ant Tree Miner classifier within a previously unknown domain and created an intrusion detection benchmark for future researchers.
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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 serious problems like false positives. By studying IDS technologies and analyzing interviews conducted with security departments at Swedish banks, this thesis identifies the major problems within IDSs today. The identified problems, together with recent IDS research reports published at the RAID 2002 symposium, are used to recommend the future direction of commercial intrusion detection systems.</p>
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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 approaches suffer from three major setbacks; limited attack detection coverage, large number of false alarms and inefficiency in operation.<br>In this thesis, we address these three issues by introducing efficient intrusion detection frameworks and models which are effective in detecting a wide variety of attacks and which result in very few false alarms. Additionally, using our approach, attacks can not only be accurately detected but can also be identified which helps to initiate effective intrusion response mechanisms in real-time. Experimental results performed on the benchmark KDD 1999 data set and two additional data sets collected locally confirm that layered conditional random fields are particularly well suited to detect attacks at the network level and user session modeling using conditional random fields can effectively detect attacks at the application level.<br>We first introduce the layered framework with conditional random fields as the core intrusion detector. Layered conditional random field can be used to build scalable and efficient network intrusion detection systems which are highly accurate in attack detection. We show that our systems can operate either at the network level or at the application level and perform better than other well known approaches for intrusion detection. Experimental results further demonstrate that our system is robust to noise in training data and handles noise better than other systems such as the decision trees and the naive Bayes. We then introduce our unified logging framework for audit data collection and perform user session modeling using conditional random fields to build real-time application intrusion detection systems. We demonstrate that our system can effectively detect attacks even when they are disguised within normal events in a single user session. Using our user session modeling approach based on conditional random fields also results in early attack detection. This is desirable since intrusion response mechanisms can be initiated in real-time thereby minimizing the impact of an attack.
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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 conscious, the networked computer hosting the telemetry data to be shared or sold also needs to be resistant to compromise. Intrusion Detection Systems (IDS) may be used to help identify and protect computers from malicious attacks in which data can be compromised.
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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 evaluate and accurate the model of intrusion detection system of different machine learning algorithms on two resampling techniques using the new CICIDS2017 dataset where Decision Trees, MLPs, and Random Forests on Stratified 10-Fold gives high stability in results with Precision, Recall, and F1-Scores of 98% and 99% with low execution times.
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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 redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these subpopulations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.
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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 the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make the detection more effective.
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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 feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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