Multi Class Machine Learning Algorithms for Intrusion Detection - A Performance Study

Manjula C. Belavagi, Balachandra Muniyal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSecurity in Computing and Communications - 5th International Symposium, SSCC 2017, Proceedings
PublisherSpringer Verlag
Pages170-178
Number of pages9
ISBN (Print)9789811068973
DOIs
Publication statusPublished - 01-01-2017
Event5th International Symposium on Security in Computing and Communications, SSCC 2017 - Manipal, India
Duration: 13-09-201716-09-2017

Publication series

NameCommunications in Computer and Information Science
Volume746
ISSN (Print)1865-0929

Conference

Conference5th International Symposium on Security in Computing and Communications, SSCC 2017
CountryIndia
CityManipal
Period13-09-1716-09-17

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    Belavagi, M. C., & Muniyal, B. (2017). Multi Class Machine Learning Algorithms for Intrusion Detection - A Performance Study. In Security in Computing and Communications - 5th International Symposium, SSCC 2017, Proceedings (pp. 170-178). (Communications in Computer and Information Science; Vol. 746). Springer Verlag. https://doi.org/10.1007/978-981-10-6898-0_14