Intrusion detection model based on GA-BP

Chen Chen; Fan Qiang; Dejiang Wan; Lintao Yang

doi:10.1117/12.2683422

26 June 2023 Intrusion detection model based on GA-BP

Chen Chen, Fan Qiang, Dejiang Wan, Lintao Yang

Proceedings Volume 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023); 127140A (2023) https://doi.org/10.1117/12.2683422
Event: Third International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 2023, Sanya, China

Abstract

In intrusion detection based on traditional BP neural network, the BP neural network algorithm model has some defects, such as easily falling into local optimum and random initial value. The selection of the initial value directly affects the training effect of BP neural network, and a better initial value is conducive to the BP neural network skipping the local optimum, thus improving the training efficiency. Aiming at the defects of BP neural network, this paper puts forward a method of optimizing the initial value of BP neural network by genetic algorithm, so that BP neural network can get a group of better initial values. The experimental results show that the application of BP neural network optimized by genetic algorithm in intrusion detection can significantly improve the detection rate and convergence rate of the algorithm.

Citation Download Citation

Chen Chen, Fan Qiang, Dejiang Wan, and Lintao Yang "Intrusion detection model based on GA-BP", Proc. SPIE 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 127140A (26 June 2023); https://doi.org/10.1117/12.2683422

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