"Network Intrusion Detection Using Multiclass Support Vector Machine" by Arvind Mewada, Prafful Gedam et al.

International Journal of Computer and Communication Technology

Article Title

Network Intrusion Detection Using Multiclass Support Vector Machine

Authors

Arvind Mewada, Computer Science department Maulana Azad Institute of National Technology, MANIT Bhopal, IndiaFollow
Prafful Gedam, Computer Science dept. Technocrat Institute of Technology, RGPV Bhopal, IndiaFollow
Shamaila Khan, Computer Science dept. Radharaman Institute of Science and Technology, RGPV Bhopal, IndiaFollow
M. Udayapal Reddy, Computer Science department Maulana Azad Institute of National Technology, MANIT Bhopal, IndiaFollow

Abstract

Intrusion detection is a topic of interest in current scenario. Statistical IDS overcomes many pitfalls present in signature based IDS. Statistical IDS uses models such as NB, C4.5 etc for classification to detect Intrusions. Multiclass Support Vector Machine is able to perform multiclass classification. This paper shows the performance of MSVM (1-versus-1, 1-versusmany and Error Correcting Output Coding (ECOC)) and it’s variants for statistical NBIDS. This paper explores the performance of MSVM for various categories of attacks

Recommended Citation

Mewada, Arvind; Gedam, Prafful; Khan, Shamaila; and Reddy, M. Udayapal (2010) "Network Intrusion Detection Using Multiclass Support Vector Machine," International Journal of Computer and Communication Technology: Vol. 1: Iss. 4, Article 7.
DOI: 10.47893/IJCCT.2010.1054
Available at: https://www.interscience.in/ijcct/vol1/iss4/7

DOI

10.47893/IJCCT.2010.1054

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