Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Jianlei Gao, Senchun Chai, Baihai Zhang and Yuanqing Xia
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Jianlei Gao: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Senchun Chai: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Baihai Zhang: School of Automation, Beijing Institute of Technology, Beijing 100081, China
Yuanqing Xia: School of Automation, Beijing Institute of Technology, Beijing 100081, China

Energies, 2019, vol. 12, issue 7, 1-17

Abstract: Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

Keywords: network intrusion detection (IDS); incremented extreme learning machine (I-ELM); adaptive-principal component analysis (A-PCA); NSL-KDD; UNSW-NB15 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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