NDLI: Associative classification and post-processing techniques used for malware detection

Content Provider	IEEE Xplore Digital Library
Author	Yanfang Ye Qingshan Jiang Weiwei Zhuang
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Xiamen Univ., Xiamen (Yanfang Ye) \|\| Software Sch., Xiamen Univ., Xiamen (Qingshan Jiang; Weiwei Zhuang)
Abstract	Numerous attacks made by the malware have presented serious threats to the security of computer users. Unfortunately, along with the development of the malware writing techniques, the number of file samples that need to be analyzed is constantly increasing on a daily basis. An automatic and robust tool to analyze and classify the file samples is the need of the hour. In this paper, resting on the analysis of Windows API execution sequences called by PE files, we use associative classification and post-processing techniques for malware detection. Promising experimental results demonstrate that the accuracy and efficiency of our malware detection method outperform popular anti-virus scanners such as Norton AntiVirus and Dr. Web, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques. In particular, the post-processing techniques we adopt can greatly reduce the number of generated rules which make it easy for the human analysts to identify the useful ones.
Starting Page	276
Ending Page	279
File Size	232364
Page Count	4
File Format	PDF
ISBN	9781424425846
DOI	10.1109/IWASID.2008.4688391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Associative Classification Data mining Windows API Sequence Support vector machines Computer science Malware Detection Post-processing Support vector machine classification Machine learning Robustness Decision trees Computer security Classification tree analysis
Content Type	Text
Resource Type	Article

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