KNN Intrusion Detection Model Based on Meta-optimization

SHEN Yanping; WU Chunhua; LUO Jie; GAO Fangping

doi:10.11936/bjutxb2018100005

Journal of Beijing University of Technology > 2020 > 46(1): 24-32. > DOI: 10.11936/bjutxb2018100005

SHEN Yanping, WU Chunhua, LUO Jie, GAO Fangping. KNN Intrusion Detection Model Based on Meta-optimization[J]. Journal of Beijing University of Technology, 2020, 46(1): 24-32. DOI: 10.11936/bjutxb2018100005

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KNN Intrusion Detection Model Based on Meta-optimization

1.
School of Information engineering, Institute of Disaster Prevention, Beijing 101601, China
2.
School of CyberSpace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
3.
Patent Examination Cooperation Sichuan Center of the Patent Office, China National Intellectual Administration Property, Chengdu 610213, China

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Received Date: October 10, 2018
Available Online: August 03, 2022
Published Date: January 09, 2020

Graphical Abstract

Abstract

Abstract

To improve the performance of intrusion detection model based on KNN, a KNN intrusion detection model using meta-optimization based on a local search algorithm for feature weighting was proposed. The differential evolution algorithm was used to optimize feature weights and the LUS based meta-optimization was selected to optimize the differential evolution. The NSL dataset was used to carry out the experiments. The proposed model was compared with that optimized by other commonly used heuristic algorithms, including GA, PSO and GWO. Results show that compared with the traditional KNN, the accuracy of the proposed method is improved by 2.86%, the detection rate increased by 3.18% and the false positive rate is reduced by 50%. The optimization based on meta-optimization is better than other optimization algorithms commonly used.

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References (31)

References

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KNN Intrusion Detection Model Based on Meta-optimization