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Rieck, K., Holz, T., Willems, C., Düssel, P. and Laskov, P. (2008) Learning and Classification of Malware Behavior. In: Zamboni, D., Ed., International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, LNCS 5137, Springer, Berlin, 108-125.

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• TITLE: Generating Rule-Based Signatures for Detecting Polymorphic Variants Using Data Mining and Sequence Alignment Approaches

  AUTHORS: Vijay Naidu, Jacqueline Whalley, Ajit Narayanan

  KEYWORDS: NNge Classifier, Gap Penalties, JS.Cassandra Virus, Polymorphic Virus, Automatic Signature Generation, Sequence Alignment, Syntactic Exploration

  JOURNAL NAME: Journal of Information Security, Vol.9 No.4, October 11, 2018

  ABSTRACT: Antiviral software systems (AVSs) have problems in detecting polymorphic variants of viruses without specific signatures for such variants. Previous alignment-based approaches for automatic signature extraction have shown how signatures can be generated from consensuses found in polymorphic variant code. Such sequence alignment approaches required variable length viral code to be extended through gap insertions into much longer equal length code for signature extraction through data mining of consensuses. Non-nested generalized exemplars (NNge) are used in this paper in an attempt to further improve the automatic detection of polymorphic variants. The important contribution of this paper is to compare a variable length data mining technique using viral source code to the previously used equal length data mining technique obtained through sequence alignment. This comparison was achieved by conducting three different experiments (i.e. Experiments I-III). Although Experiments I and II generated unique and effective syntactic signatures, Experiment III generated the most effective signatures with an average detection rate of over 93%. The implications are that future, syntactic-based smart AVSs may be able to generate effective signatures automatically from malware code by adopting data mining and alignment techniques to cover for both known and unknown polymorphic variants and without the need for semantic (run-time) analysis.