Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS

Key Words:Network intrusion detection SVM Kernel method Game theory Nash equilibrium

Abstract:Network Intrusion Detection (NID), an important topic in the field of information security, can be viewed as a pattern recognition problem. The existing pattern recognition methods can achieve a good performance when the number of training samples is large enough. However, modern network attacks are diverse and constantly updated, and the training samples have much smaller size. Furthermore, to improve the learning ability of SVM, the research of kernel functions mainly focus on the selection, construction and improvement of kernel functions. Nonetheless, in practice, there are no theories to solve the problem of the construction of kernel functions perfectly. In this paper, we effectively integrate the advantages of the radial basis function kernel and the polynomial kernel on the notion of the game theory and propose a novel kernel SVM algorithm with game theory for NID, called GTNID-SVM. The basic idea is to exploit the game theory in NID to get a SVM classifier with better learning ability and generalization performance. To the best of our knowledge, GTNID-SVM is the first algorithm that studies ensemble kernel function with game theory in NID. We conduct empirical studies on the DARPA dataset, and the results demonstrate that the proposed approach is feasible and more effective.

ISSN No.:1976-7277

Translation or Not:no

Date of Publication:2017-08-31

Co-author:刘予飞

Correspondence Author:Pi Dechang