Abstract
Advancement of the network technology has increased our dependency on the Internet. Hence the security of the network plays a very important role. The network intrusions can be identified using Intrusion Detection System (IDS). Machine learning algorithms are used to predict the network behavior as intrusion or normal. This paper discusses the prediction analysis of different supervised machine learning algorithms namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine and Random Forest on NSL-KDD dataset. These machine learning classification techniques are used to predict the four different types of attacks namely Denial of Service attack, Remote to Local (R2L), Probe and User to Root(U2R) attacks using multi-class classification technique.
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Belavagi, M.C., Muniyal, B. (2017). Multi Class Machine Learning Algorithms for Intrusion Detection - A Performance Study. In: Thampi, S., Martínez Pérez, G., Westphall, C., Hu, J., Fan, C., Gómez Mármol, F. (eds) Security in Computing and Communications. SSCC 2017. Communications in Computer and Information Science, vol 746. Springer, Singapore. https://doi.org/10.1007/978-981-10-6898-0_14
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