Static Detection and Simulation of Malicious Code in a Metallurgical Master Control Station Based on Behavior Information Gain

Authors

  • Tingfeng Hu Wuxi City College of Vocational Technology, Wuxi 214153, China

Abstract

This paper uses data mining technology to detect malicious code, and proposes a feature selection method based on behavior information gain. Considering the function of feature frequency and information gain, the proposed method can select the most effective features more accurately, and improve the detection performance, thus realizing a malicious code detection system, N-gram and variable length N-gram binary codes are used as the feature extraction method. As the feature selection method, the method of information gain uses several classifiers to detect malicious codes. Experimental results show that this method can effectively improve the accuracy and detection rate of detecting malicious code.

Keywords: Behavior information gain; Malicious code; Static detection; Simulation

T. Hu, “Static Detection and Simulation of Malicious Code in a Metallurgical Master Control Station Based on Behavior Information Gainâ€, Engineering Intelligent Systems, vol. 28 no. 1,
pp. 15-22, 2020.

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Published

2020-03-01

Issue

Vol. 28 No. 1 (2020): International Journal of Engineering Intelligent Systems

Section

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