Selective Ensemble of KELM-Based Complex Network Intrusion Detection

doi:10.3969/j.issn.0372-2112.2019.05.014

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ACTA ELECTRONICA SINICA ›› 2019, Vol. 47 ›› Issue (5) : 1070-1078. DOI: 10.3969/j.issn.0372-2112.2019.05.014

Selective Ensemble of KELM-Based Complex Network Intrusion Detection

LIU Jin-ping^1,2, HE Jie-zhou¹, MA Tian-yu³, ZHANG Wu-xia¹, TANG Zhao-hui⁴, XU Peng-fei¹

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Abstract

To solve the problem of the low detection accuracy of new intrusions with long detection time due to the complex and changeable nature of network intrusions,this paper proposes a network intrusion detection method based on the selective learning of Kernel Extreme Learning Machines (KELMs).First,based on the high efficiency learning characteristics of the single KELM learner,multiple KELMs are trained independently by the Bagging strategy.Then,based on the margin distance minimization (MDM) guidelines,KELM learners are integrated by selecting a part of them with high gains based on the MDM-based gain measures.Extensive validation and comparative experiments on the the KDD99 data set and on a hybrid network simulation platform mixed with wireless networks and Ethernet networks demonstrate that the proposed method achieves better recognition accuracies with faster recognition speed than the network intrusion detection methods based on the single learner and the traditional ensemble learning,which can effectively detect the known and unknown network intrusion connection in real time.

Key words

network intrusion detection / extreme learning machine (ELM) / anomaly detection / selective ensemble learning / margin distance minimization

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LIU Jin-ping, HE Jie-zhou, MA Tian-yu, ZHANG Wu-xia, TANG Zhao-hui, XU Peng-fei. Selective Ensemble of KELM-Based Complex Network Intrusion Detection[J]. Acta Electronica Sinica, 2019, 47(5): 1070-1078. https://doi.org/10.3969/j.issn.0372-2112.2019.05.014

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Funding

National Natural Science Foundation of China (No.61501183, No.61771492, No.61472134); Key Program of NSFC - Guangdong Joint Fund (No.U1701261); Natural Science Foundation of Hunan Province, China (No.2018JJ3349); Postgraguate Technology Innovation Program of Hunan Province (No.CX2018B312)