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Quantum beetle swarm algorithm optimized extreme learning machine for intrusion detection

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Abstract

Because of the low accuracy in intrusion detection, a model of extreme learning machine based on the optimization of quantum beetle swarm algorithm is proposed. First of all, this paper proposes a quantum beetle swarm optimization algorithm, which introduces quantum mechanics and combines the advantages of beetle antennae search and particle swarm optimization. In this way, the individual can learn both their own experience and group experience, which enables the beetle to move purposefully and instructively, and improves the convergence performance of the algorithm. In extreme learning machine, it is more difficult to solve the problem in high-dimensional data. This paper proposed an improved extreme learning machine that uses the least squares QR algorithm to decompose the matrix, which can reduce the computational complexity of the traditional extreme learning machine. The improved extreme learning machine model optimized by quantum beetle swarm optimization algorithm is applied to intrusion detection, and the simulation results show that the model proposed in this paper can significantly improve detection accuracy and increase convergence rate.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Nos. 61772295, 61572270, and 61173056), the PHD foundation of Chongqing Normal University 576 (No. 19XLB003), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202000501), Chongqing Technology Innovation and application devel opment special general project (cstc2020jscx-lyjsAX0002) and Shandong Technology Innovation Guidance Program under Grant 2020LYXZ023.

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  1. College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China

    Yumin Dong, Wanbin Hu, Jinlei Zhang, Min Chen, Wei Liao & Zhengquan Chen

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  1. Yumin Dong
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  2. Wanbin Hu
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  3. Jinlei Zhang
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Correspondence to Yumin Dong.

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Dong, Y., Hu, W., Zhang, J. et al. Quantum beetle swarm algorithm optimized extreme learning machine for intrusion detection. Quantum Inf Process 21, 9 (2022). https://doi.org/10.1007/s11128-021-03311-w

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