Network Intrusion Detection Method Based on Improved Multi-factorialOptimization Bat Algorithm

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Network Intrusion Detection Method Based on Improved Multi-factorialOptimization Bat Algorithm

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[1]ZHANG Zhen,ZHANG Siyuan,TIAN Hongpeng.Network Intrusion Detection Method Based on Improved Multi-factorialOptimization Bat Algorithm[J].Journal of Zhengzhou University (Engineering Science),2024,45(05):52-60.[doi:10.13705/j.issn.1671-6833.2024.05.015]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 05 Page number: 52-60 Column: Public date: 2024-08-08

Title:: Network Intrusion Detection Method Based on Improved Multi-factorialOptimization Bat Algorithm

Author(s):: ZHANG Zhen; ZHANG Siyuan; TIAN Hongpeng; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:: intrusion detection; cyber security; feature selection; bat algorithm; multi-factorial optimization

CLC:: TP181

DOI:: 10.13705/j.issn.1671-6833.2024.05.015

Abstract:: In addressing the challenge of diminished intrusion detection accuracy resulting from the abundance ofredundant and irrelevant features in high-dimensional network data, an improved multi-factorial optimization bat algorithm ( IMFBA) was introduced for precise data feature selection, with the ultimate goal of improving network intrusion detection accuracy. Within the multi-factorial optimization framework, global and local feature selectiontasks were formulated. Information exchange between these tasks was facilitated by selection and vertical culturaltransmission operators, strategically designed based on the bat algorithm. The global feature selection task was accelerated in identifying optimal solution spaces, thereby enhancing the algorithm′s convergence speed and stability.By incorporating the reverse learning strategy and differential evolution into the bat algorithm, the initial solution selection stage and individual updating process were refined to address the absence of a mutation mechanism, fostering solution diversity and aiding the algorithm in escaping local optima. An adaptive parameter adjustment strategywas introduced, determining weightings for guiding individual updates based on potential optimal solution quality.This could mitigate the risk of knowledge negative transfer during multi-task feature selection, achieving a balancebetween global exploration and local exploitation. The feature subsets selected by IMFBA demonstrate classificationaccuracy of 95. 37% and 85. 14% on the KDD CUP 99 and NSL-KDD intrusion detection datasets, respectively.This reflected increased by 3. 01 percentage points and 9. 78 percentage points compared to the complete dataset.Experiment results confirm the efficacy of EMFBA in selecting higher-quality feature subsets and, consequently,enhancing network intrusion detection accuracy.

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Last Update: 2024-09-02