A Comprehensive Evaluation of Machine Learning Algorithms for Network Security Intrusion Detection Systems (IDS)

Ezekiel Ehime Agbon, Abdulkadir Mohammed Giwa, Opeyemi Olajumoke Adekogbe, Habib Tunji Adeyemo, Mohammed Nasir, Abiodun Mogaji Babatunde

Abstract


Networks are faced with a wide range of difficulties and threats as a result of the development of Artificial intelligence (AI) technologies and the advent of fifth-generation networks. Concerns about data confidentiality and general network security emerge as more users access the network at once. An Intrusion Detection System (IDS) is frequently used to identify potential threats in order to allay these worries. Network assaults can be divided into two categories: minor and major. Denial-of-Service (DoS) and prob assaults are major types of attacks, whereas User-to-Root (U2R) and Remote-to-Login (R2L) attacks are smaller types. Minor attacks, usually referred to as uncommon attacks, pose a serious risk to host systems and can be particularly difficult to identify. This study investigates several machine learning techniques used in the implementation of these IDS systems and provides an extensive survey on IDS including proposed solution to each of the ML algorithm for IDS.


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References


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