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

Intrusion Detection Based on Self-adaptive Differential Evolutionary Extreme Learning Machine AbstractNowadays 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Advanced Network Monitoring and Controls de Gruyter

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

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Publisher
de Gruyter
Copyright
© 2017 Junhua Ku et al., published by Sciendo
eISSN
2470-8038
DOI
10.21307/ijanmc-2017-057
Publisher site
See Article on Publisher Site

Abstract

AbstractNowadays 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.

Journal

International Journal of Advanced Network Monitoring and Controls – de Gruyter

Published: Jan 1, 2017

Keywords: Extreme learning machines; Differential evolution extreme learning machines; Self-adaptive differential evolution extreme learning machines; Intrusion detection; Network security

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