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

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International Journal of Advanced Network, Monitoring and Controls

Xi'an Technological University

Subject: Computer Science, Software Engineering

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eISSN: 2470-8038

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VOLUME 2 , ISSUE 3 (September 2017) > List of articles

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

Junhua Ku / Bing Zheng / Dawei Yun

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

Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 3, Pages 54-60, DOI: https://doi.org/10.1109/iccnea.2017.57

License : (CC BY-NC-ND 4.0)

Published Online: 11-April-2018

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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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