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Detection of unknown computer worms based on behavioral classification of the host

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  • Moskovitch, Robert
  • Elovici, Yuval
  • Rokach, Lior

Abstract

Machine learning techniques are widely used in many fields. One of the applications of machine learning in the field of information security is classification of a computer behavior into malicious and benign. Antiviruses consisting of signature-based methods are helpless against new (unknown) computer worms. This paper focuses on the feasibility of accurately detecting unknown worm activity in individual computers while minimizing the required set of features collected from the monitored computer. A comprehensive experiment for testing the feasibility of detecting unknown computer worms, employing several computer configurations, background applications, and user activity, was performed. During the experiments 323 computer features were monitored by an agent that was developed. Four feature selection methods were used to reduce the number of features and four learning algorithms were applied on the resulting feature subsets. The evaluation results suggest that by using classification algorithms applied on only 20 features the mean detection accuracy exceeded 90%, and for specific unknown worms accuracy reached above 99%, while maintaining a low level of false positive rate.

Suggested Citation

  • Moskovitch, Robert & Elovici, Yuval & Rokach, Lior, 2008. "Detection of unknown computer worms based on behavioral classification of the host," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4544-4566, May.
  • Handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4544-4566
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    References listed on IDEAS

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    1. Cai, D. Michael & Gokhale, Maya & Theiler, James, 2007. "Comparison of feature selection and classification algorithms in identifying malicious executables," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3156-3172, March.
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    Cited by:

    1. Rokach, Lior, 2009. "Collective-agreement-based pruning of ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1015-1026, February.
    2. D. Thorleuchter & D. Van Den Poel, 2012. "Improved Multilevel Security with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/811, Ghent University, Faculty of Economics and Business Administration.
    3. Menahem, Eitan & Shabtai, Asaf & Rokach, Lior & Elovici, Yuval, 2009. "Improving malware detection by applying multi-inducer ensemble," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1483-1494, February.
    4. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.

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    1. Menahem, Eitan & Shabtai, Asaf & Rokach, Lior & Elovici, Yuval, 2009. "Improving malware detection by applying multi-inducer ensemble," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1483-1494, February.

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