Belavagi, Manjula C and Muniyal, Balachandra (2017) Multi Class Machine Learning Algorithms forIntrusion Detection - A Performance Study. Communications in Computer and Information Science (Information Processing and Management). ISSN 1865-0929

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Abstract

Advancement of the network technology has increased our dependency on the Internet. Hence the security of the network plays avery important role. The network intrusions can be identi�ed using In-trusion Detection System (IDS). Machine learning algorithms are usedto predict the network behavior as intrusion or normal. This paper dis- cusses the prediction analysis of di�erent supervised machine learningalgorithms namely Logistic Regression, Gaussian Naive Bayes, SupportVector Machine and Random Forest on NSL-KDD dataset. These ma-chine learning classi�cation techniques are used to predict the four dif- ferent types of attacks namely Denial of Service attack, Remote to Local(R2L), Probe and User to Root(U2R) attacks using multi-class classi�-cation technique.

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