中圖分類號： TP393
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.12.006
引用格式： 楊忠君，鄭志權，敖然，等. 基于改進麻雀算法的工控入侵檢測方法[J].信息技術與網絡安全，2021，40(12)：32-39.

Industrial control intrusion detection method based on improved sparrow algorithm

Abstract： In order to solve the problem of how to select the most effective feature set of industrial control intrusion data and improve the performance of intrusion detection, a new industrial control intrusion detection method(ISSA-TWSVM) based on improved sparrow search algorithm(ISSA) and twin support vector machine(TWSVM) is proposed. ISSA uses cubic chaotic mapping to initialize the population and introduces dynamic inertia weight. It combines the counterpotential differential evolution strategy and Cauchy mutation operator to cross mutate the current optimal solution, so as to enhance the ability of sparrow algorithm to search globally and jump out of local optimization. The excellent performance of ISSA is proved in the benchmark function. Then, after optimizing the industrial control feature subset based on ISSA, the data features are reduced, and TWSVM is used to classify the industrial control data. The experimental results on the industrial control network standard data set published by MSU show that ISSA-TWSVM can quickly extract the optimal feature subset and greatly improve the detection performance of the algorithm.

Key words : sparrow search algorithm；chaos operator；dynamic inertia weight；Cauchy operator；opposition-based differential evolution；industrial control intrusion detection