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Books on the topic 'Host intrusion detection system'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Tsukerman, Emmanuel. Designing a Machine Learning Intrusion Detection System. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6591-8.

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2

Ingram, Dennis J. Autonomous agents for distributed intrusion detection in a multi-host environment. Naval Postgraduate School, 1999.

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3

Herrero, Alvaro. Mobile Hybrid Intrusion Detection: The MOVICAB-IDS System. Springer Berlin Heidelberg, 2011.

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4

Barrus, Joseph D. Intrusion detection in real-time in a multi-node, multi-host environment. Naval Postgraduate School, 1997.

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5

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

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6

Spenneberg, Ralf. Intrusion Detection fu r Linux-Server: Mit Open-Source-Tools Angriffe erkennen und analysieren ; mit einer Einfu hrung in die digitale Forensik. Markt-und-Technik-Verl., 2003.

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7

Lhotsky, Brad. Instant OSSEC Host-Based Intrusion Detection System. Packt Publishing, Limited, 2013.

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8

Instant Ossec Host-Based Intrusion Detection System. Packt Publishing, Limited, 2013.

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9

Network Defense-in-Depth: Evaluating Host-Based Intrusion Detection Systems. Storming Media, 2001.

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10

OSSEC Host-Based Intrusion Detection Guide. Elsevier, 2008. http://dx.doi.org/10.1016/b978-1-59749-240-9.x0001-4.

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11

OSSEC Host-Based Intrusion Detection Guide. Syngress, 2008.

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12

Hay, Andrew, Daniel Cid, and Rory Bray. OSSEC Host-Based Intrusion Detection Guide. Elsevier Science & Technology Books, 2008.

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13

Carter, Earl. Cisco Secure Intrusion Detection System. Cisco Press, 2001.

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14

Swartzbaugh, Richard Andrew. Internals of an Intrusion Detection System. Primedia eLaunch LLC, 2018.

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15

Marrinan, Jean. Remote Sensor Monitoring : the Home Intruder Detection System: Intrusion Detection. Independently Published, 2021.

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16

Autonomous Agents for Distributed Intrusion Detection in a Multi-Host Environment. Storming Media, 1999.

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17

Corchado, Emilio, and Álvaro Herrero. Mobile Hybrid Intrusion Detection: The MOVICAB-IDS System. Springer, 2014.

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18

Blokdyk, Gerardus. Intrusion Detection System a Complete Guide - 2020 Edition. Emereo Pty Limited, 2020.

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19

Network Security: Distributed Approach in Intrusion Detection System. Vyusta Ventures LLP, 2022.

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20

Intrusion Detection in Real Time in a Multi-Mode, Multi-Host Environment. Storming Media, 1997.

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21

Intrusion Detection System Evasion durch Angriffsverschleierung in Exploiting Frameworks. Diplomica Verlag, 2010.

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22

Intrusion Detection System Evasion durch Angriffsverschleierung in Exploiting Frameworks. Thomas Stein, 2010.

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23

(Editor), Gregory J. Kirmeyer, Melinda Friedman (Editor), Katherine Martel (Editor), et al., eds. Pathogen Intrusion into the Distribution System. American Water Works Association, 2001.

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24

Rajendran, Praveen Kumar. Hybrid Intrusion Detection System for Private Cloud: From Research Perspective. Independently Published, 2018.

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25

Toxen, Bob. Real World Linux Security: Intrusion Prevention, Detection and Recovery. Prentice Hall PTR, 2000.

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26

Toxen, Bob. Real World Linux Security: Intrusion Prevention, Detection and Recovery. Prentice Hall PTR, 2000.

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27

Gondaliya, Tapan. Intrusion Detection System in Mobile Ad Hoc Network in Mac Layer. GRIN Verlag GmbH, 2014.

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28

CCSP Self-Study: Cisco Secure Intrusion Detection System (CSIDS) (2nd Edition) (Self-Study Guide). 2nd ed. Cisco Press, 2004.

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29

Using a Distributed Object-Oriented Database Management System in Support of a High-Speed Network Intrusion Detection System Data Repository. Storming Media, 2001.

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30

Nils Otto vor dem gentschen Felde and Nils Otto Vor Dem Gentschen Felde. Ein föderiertes Intrusion Detection System für Grids: Dissertation an der Fakultät für Mathematik, Informatik und Statistik der Ludwig-Maximilians-Universität München. Books on Demand GmbH, 2009.

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31

Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.

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Abstract:

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

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