Abstract
The growth of the Internet and the opening of systems have led to an increasing number of attacks on computer networks. Security vulnerabilities are increasing, in the design of communication protocols as well as in their implementation. On another side, the knowledge, tools and scripts, to launch attacks, become readily available and more usable. Hence, the need for an intrusion detection system (IDS) is also more apparent. This technology consists in searching for a series of words or parameters characterizing an attack in a packet flow. Intrusion Detection Systems has become an essential and critical component in an IT security architecture. An IDS should be designed as part of a global security policy. The objective of an IDS is to detect any violation of the rules according to the local security policy, it thus makes it possible to report attacks. This last multi-faceted, difficult to pin down when not handled, but most of the work done in this area remains difficult to compare, that's why the aim of our article is to analyze and compare intrusion detection techniques with several machine learning algorithms. Our research indicates which algorithm offers better overall performance than the others with the IDS field.
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Elaeraj, O., Leghris, C., Renault, É. (2021). Performance Evaluation of Some Machine Learning Algorithms for Security Intrusion Detection. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2020. Lecture Notes in Computer Science(), vol 12629. Springer, Cham. https://doi.org/10.1007/978-3-030-70866-5_10
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