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A Review of Classification Approaches Using Support Vector Machine in Intrusion Detection

  • Conference paper
Informatics Engineering and Information Science (ICIEIS 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 253))

Abstract

Presently, Network security is the most concerned subject matter because with the rapid use of internet technology and further dependence on network for keeping our data secure, it’s becoming impossible to protect from vulnerable attacks. Intrusion detection systems (IDS) are the key solution for detecting these attacks so that the network remains reliable. There are different classification approaches used to implement IDS in order to increase their efficiency in terms of detection rate. Support vector machine (SVM) is used for classification in IDS due to its good generalization ability and non linear classification using different kernel functions and performs well as compared to other classifiers. Different Kernels of SVM are used for different problems to enhance performance rate. In this paper, we provide a review of the SVM and its kernel approaches in IDS for future research and implementation towards the development of optimal approach in intrusion detection system with maximum detection rate and minimized false alarms.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750, Tronoh, Perak, Malaysia

    Noreen Kausar & Azween Abdullah

  2. Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750, Tronoh, Perak, Malaysia

    Brahim Belhaouari Samir

  3. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, 11543, Riyadh, Kingdom of Saudi Arabia

    Iftikhar Ahmad

  4. Department of Computer Science, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Mohammad Hussain

Authors
  1. Noreen Kausar
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  2. Brahim Belhaouari Samir
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  3. Azween Abdullah
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  4. Iftikhar Ahmad
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  5. Mohammad Hussain
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Editor information

Editors and Affiliations

  1. Advanced Informatics School (UTM AIS), UTM International Campus, 54100, Kuala Lumpur, Malaysia

    Azizah Abd Manaf , Shamsul Sahibuddin , Rabiah Ahmad  & Salwani Mohd Daud , ,  & 

  2. Information Systems Department, King Saud University, Riyadh, Saudi Arabia

    Eyas El-Qawasmeh

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© 2011 Springer-Verlag Berlin Heidelberg

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Kausar, N., Belhaouari Samir, B., Abdullah, A., Ahmad, I., Hussain, M. (2011). A Review of Classification Approaches Using Support Vector Machine in Intrusion Detection. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25462-8_3

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  • DOI: https://doi.org/10.1007/978-3-642-25462-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25461-1

  • Online ISBN: 978-3-642-25462-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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