A Novel Anti-Obfuscation Model for Detecting Malicious Code
Yuehan Wang, Tong Li, Yongquan Cai, Zhenhu Ning, Fei Xue, Di Jiao · International Journal of Open Source Software and Processes · 2017
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.