SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging
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- 作者：Yanfang Ye (1) yeyanfang@yahoo.com.cn
Lifei Chen (2) cliphy@sina.com
Dingding Wang (3) dwang003@cs.fiu.edu
Tao Li (3) taoli@cs.fiu.edu
Qingshan Jiang (4) qjiang@xmu.edu.cn
Min Zhao (5) zhaomin@kingsoft.com - 刊名：Journal in Computer Virology
- 出版时间：November 2009
- 出版年：2009
- 期刊代码：pc401_1772-9890
- 类别：cp
- 卷：5
- 期：4
- 页码：283-293
- 数据来源：sp
摘要
Malicious executables are programs designed to infiltrate or damage a computer system without the owner’s consent, which have become a serious threat to the security of computer systems. There is an urgent need for effective techniques to detect polymorphic, metamorphic and previously unseen malicious executables of which detection fails in most of the commercial anti-virus software. In this paper, we develop interpretable string based malware detection system (SBMDS), which is based on interpretable string analysis and uses support vector machine (SVM) ensemble with Bagging to classify the file samples and predict the exact types of the malware. Interpretable strings contain both application programming interface (API) execution calls and important semantic strings reflecting an attacker’s intent and goal. Our SBMDS is carried out with four major steps: (1) first constructing the interpretable strings by developing a feature parser; (2) performing feature selection to select informative strings related to different types of malware; (3) followed by using SVM ensemble with bagging to construct the classifier; (4) and finally conducting the malware detector, which not only can detect whether a program is malicious or not, but also can predict the exact type of the malware. Our case study on the large collection of file samples collected by Kingsoft Anti-virus lab illustrate that: (1) The accuracy and efficiency of our SBMDS outperform several popular anti-virus software; (2) Based on the signatures of interpretable strings, our SBMDS outperforms data mining based detection systems which employ single SVM, Naive Bayes with bagging, Decision Trees with bagging; (3) Compared with the IMDS which utilizes the objective-oriented association (OOA) based classification on API calls, our SBMDS achieves better performance. Our SBMDS system has already been incorporated into the scanning tool of a commercial anti-virus software.