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Journal in Computer Virology

, Volume 4, Issue 4, pp 323–334 | Cite as

An intelligent PE-malware detection system based on association mining

  • Yanfang Ye
  • Dingding Wang
  • Tao Li
  • Dongyi Ye
  • Qingshan Jiang
Original Paper

Abstract

The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic/metamorphic and new, previously unseen malicious executables. Data mining methods such as Naive Bayes and Decision Tree have been studied on small collections of executables. In this paper, resting on the analysis of Windows APIs called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of KingSoft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system outperform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques. Our system has already been incorporated into the scanning tool of KingSoft’s Anti-Virus software.

Keywords

Support Vector Machine Association Rule Application Program Interface Frequent Itemsets Malicious Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag France 2008

Authors and Affiliations

  • Yanfang Ye
    • 1
  • Dingding Wang
    • 2
  • Tao Li
    • 2
  • Dongyi Ye
    • 3
  • Qingshan Jiang
    • 4
  1. 1.Department of Computer ScienceXiamen UniversityXiamenPeople’s Republic of China
  2. 2.School of Computer ScienceFlorida International UniversityMiamiUSA
  3. 3.College of Maths and Computer ScienceFuzhou UniversityFuzhouPeople’s Republic of China
  4. 4.Software SchoolXiamen UniversityXiamenPeople’s Republic of China

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