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Intrusion Detection Method Based on Minkowski Distance Negative Selection

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 153))

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Abstract

Negative selection algorithm is considered as the dominant and important method to realize the intrusion detection because of its excellent adaptive protection mechanism. However, the traditional algorithm does not match the characteristics of the “self” sequence, which often causes the failure to detect the “non-self” pattern sequence, resulting in the “black hole” phenomenon. Therefore, aiming at the defects of too many “black holes” in the negative selection algorithm, this paper proposes an intrusion detection method based on Minkowski distance negative selection. The technique first calculates the Minkowski distance between the detector sequences, and measures the same number of bits between the detector and the self-set. The trained new detectors are input into the mature detector set for integration. Simulation results show that compared with the traditional negative selection algorithm, this method greatly reduces the black holes, the false alarm rate, and improves the detection efficiency.

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Authors and Affiliations

  1. Cryptology Engineering Institute, Engineering University of People’s Armed Police Force, Xi’an, 710086, China

    Yang Lei & Wenxuan Bie

Authors
  1. Yang Lei
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  2. Wenxuan Bie
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Corresponding author

Correspondence to Yang Lei .

Editor information

Editors and Affiliations

  1. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UK

    Maozhen Li

  3. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Kenli Li

  4. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Zheng Xiao

  5. College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China

    Longlong Liao

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Lei, Y., Bie, W. (2023). Intrusion Detection Method Based on Minkowski Distance Negative Selection. In: Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_115

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