Intrusion Detection System Based on Multi-class SVM | Zendy

Hansung Lee | Zendy; Jiyoung Song | Zendy; Daihee Park | Zendy

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Intrusion Detection System Based on Multi-class SVM

Author(s) -

Hansung Lee,

Jiyoung Song,

Daihee Park

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28660-8

DOI - 10.1007/11548706_54

Subject(s) - computer science , intrusion detection system , support vector machine , class (philosophy) , artificial intelligence , pattern recognition (psychology) , data mining

In this paper, we propose a new intrusion detection system: MMIDS (Multi-step Multi-class Intrusion Detection System), which alleviates some drawbacks associated with misuse detection and anomaly detection. The MMIDS consists of a hierarchical structure of one-class SVM, novel multi-class SVM, and incremental clustering algorithm: Fuzzy-ART. It is able to detect novel attacks, to give detail informations of attack types, to provide economic system maintenance, and to provide incremental update and extension with a system.

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