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GECCO
2009
Springer

On the appropriateness of evolutionary rule learning algorithms for malware detection

9 years 7 months ago
On the appropriateness of evolutionary rule learning algorithms for malware detection
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classification problem of detecting malicious executables. The executable dataset, used in this study, consists of 189 attributes which are statically extracted from the executables of Microsoft Windows operating system. In our study, we compare the performance of rule learning algorithms with respect to four metrics: (1) classification accuracy, (2) the number of rules in the developed rule set, (3) the comprehensibility of the generated rules, and (4) the processing overhead of the rule learning process. The results of our comparative study suggest that evolutionary rule learning classifiers cannot be deployed in real-world malware detection systems. Categories and Subject Descriptors D.4.6 [Software]: Security and Protection—Invasive software; I.2.6 [Artificial Intelligence]: Learning—Concept Learning, In...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where GECCO
Authors M. Zubair Shafiq, S. Momina Tabish, Muddassar Farooq
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