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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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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DOI: https://doi.org/10.1007/978-3-031-20738-9_115
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