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A Novel Anti-Obfuscation Model for Detecting Malicious Code

A Novel Anti-Obfuscation Model for Detecting Malicious Code

Yuehan Wang, Tong Li, Yongquan Cai, Zhenhu Ning, Fei Xue, Di Jiao
Copyright: © 2017 |Volume: 8 |Issue: 2 |Pages: 19
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781522512684|DOI: 10.4018/IJOSSP.2017040102
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MLA

Wang, Yuehan, et al. "A Novel Anti-Obfuscation Model for Detecting Malicious Code." IJOSSP vol.8, no.2 2017: pp.25-43. http://doi.org/10.4018/IJOSSP.2017040102

APA

Wang, Y., Li, T., Cai, Y., Ning, Z., Xue, F., & Jiao, D. (2017). A Novel Anti-Obfuscation Model for Detecting Malicious Code. International Journal of Open Source Software and Processes (IJOSSP), 8(2), 25-43. http://doi.org/10.4018/IJOSSP.2017040102

Chicago

Wang, Yuehan, et al. "A Novel Anti-Obfuscation Model for Detecting Malicious Code," International Journal of Open Source Software and Processes (IJOSSP) 8, no.2: 25-43. http://doi.org/10.4018/IJOSSP.2017040102

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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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