Intrusion Detection Based on Self-adaptive Differential Evolutionary Extreme Learning Machine

Ku, Junhua; Zheng, Bing; Yun, Dawei

doi:10.21307/ijanmc-2017-057

Abstract

Nowadays with the rapid development of network-based services and users of the internet in everyday life, intrusion detection becomes a promising area of research in the domain of security. Intrusion detection system (IDS) can detect the intrusions of someone who is not authorized to the present computer system automatically, so intrusion detection system has emerged as an essential component and an important technique for network security.

Extreme learning machine (ELM) is an interested area of research for detecting possible intrusions and attacks. In this paper, we propose an improved learning algorithm named self- adaptive differential evolution extreme learning machine (SADE-ELM) for classifying and detecting the intrusions. We compare our methods with commonly used ELM, DE-ELM techniques in classifications. Simulation results show that the proposed SADE-ELM approach achieves higher detection accuracy in classification case.

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Intrusion Detection Based on Self-adaptive Differential Evolutionary Extreme Learning Machine

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