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Intrusion Detection Based on Self-adaptive Differential Evolution Extreme Learning Machine with Gaussian Kernel

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Parallel Architecture, Algorithm and Programming (PAAP 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 729))

Abstract

In our everyday life, intrusion detection system(IDS) becomes a promising area of research in the domain of security. With the rapid development of network-based services, IDS can detect the intruders who are not authorized to the present computer system, so IDS has emerged as an essential component and an important technique for network security.

In order to conquer the disadvantage of the traditional algorithm for single-hidden layer feedforward neural network (SLFN), an improved algorithm, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. 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 with Gaussian Kernel (SaDE-KELM) 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-KELM approach achieves higher detection accuracy in classification case.

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Acknowledgement

This research was supported by key science research project of Education Department of Hainan province (Hnky2017ZD-20).

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Authors and Affiliations

  1. Department of Information Engineering, Hainan Institute of Science and Technology, Haikou, 571100, China

    Junhua Ku & Bing Zheng

Authors
  1. Junhua Ku
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  2. Bing Zheng
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Correspondence to Junhua Ku .

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Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China

    Guoliang Chen

  2. Sun Yat-sen University, Guangzhou, Guangdong, China

    Hong Shen

  3. Hainan University, Haikou, Hainan, China

    Mingrui Chen

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Ku, J., Zheng, B. (2017). Intrusion Detection Based on Self-adaptive Differential Evolution Extreme Learning Machine with Gaussian Kernel. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_2

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  • DOI: https://doi.org/10.1007/978-981-10-6442-5_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6441-8

  • Online ISBN: 978-981-10-6442-5

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