Research on the Application of AR-OSELM Algorithm in Network Intrusion Detection

doi:10.3969/j.issn.1671-1122.2018.06.001

Abstract

Abstract:

Considering the low learning efficiency and poor detection precision of the traditional learning algorithms caused by the redundant attributes of the incremental network intrusion data, this paper proposes an online sequential extreme learning machine algorithm based on attributes reduction in rough set (AR-OSELM). Firstly, the attribute kernels are obtained by using the methods of rough set positive domain and discernibility matrix on intrusion data ,thus characteristic collections of non-redundant attributes are obtained. Then using the online sequential extreme learning machine as the classification algorithm to classify the data sets. The results of the simulation experiment show that that the AR-OSELM algorithm is more efficient in learning and training incremental data and has lower error rates with comparison to BP, ELM and HELM algorithms. The AR-OSELM algorithm has better ability of generalization than other tradition algorithms which provides a new method for network intrusion detection.

Key words: network intrusion detection, rough set, attributes reduction, OSELM

CLC Number:

TP309

Shuning WEI, Xingru CHEN, Yong JIAO, Jin WANG. Research on the Application of AR-OSELM Algorithm in Network Intrusion Detection[J]. Netinfo Security, 2018, 18(6): 1-6.

Figures/Tables 7

References 10

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