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A Novel Anti-Obfuscation Model for Detecting Malicious Code
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Author(s): Yuehan Wang (Beijing University of Technology, Beijing, China), Tong Li (Beijing University of Technology, Beijing, China), Yongquan Cai (Beijing University of Technology, Beijing, China), Zhenhu Ning (Beijing University of Technology, Beijing, China), Fei Xue (Beijing Wuzi University, Beijing, China)and Di Jiao (National Engineering Laboratory for e-Government Integration and Application, Beijing, China)
Copyright: 2020
Pages: 21
Source title:
Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch080
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Abstract
In this article, the authors present a new malicious code detection model. The detection model improves typical n-gram feature extraction algorithms that are easy to be obfuscated. Specifically, the proposed model can dynamically determine obfuscation features and then adjust the selection of meaningful features to improve corresponding machine learning analysis. The experimental results show that the feature database, which is built based on the proposed feature selection and cleaning method, contains a stable number of features and can automatically get rid of obfuscation features. Overall, the proposed detection model has features of long timeliness, high applicability and high accuracy of identification.
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