Exploring Timeline-Based Malware Classification

Abstract : Over the decades or so, Anti-Malware (AM) communities have been faced with a substantial increase in malware activity, including the development of ever-more-sophisticated methods of evading detection. Researchers have argued that an AM strategy which is successful in a given time period cannot work at a much later date due to the changes in malware design. Despite this argument, in this paper, we convincingly demonstrate a malware detection approach, which retains high accuracy over an extended time period. To the best of our knowledge, this work is the first to examine malware executables collected over a span of 10 years.  By combining both static and dynamic features of malware and cleanware, and accumulating these features over intervals in the 10-year period in our test, we construct a high accuracy malware detection method which retains almost steady accuracy over the period. While the trend is a slight down, our results strongly support the hypothesis that perhaps it is possible to develop a malware detection strategy that can work well enough into the future.
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Lech J. Janczewski; Henry B. Wolfe; Sujeet Shenoi. 28th Security and Privacy Protection in Information Processing Systems (SEC), Jul 2013, Auckland, New Zealand. Springer, IFIP Advances in Information and Communication Technology, AICT-405, pp.1-13, 2013, Security and Privacy Protection in Information Processing Systems. 〈10.1007/978-3-642-39218-4_1〉
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Rafiqul Islam, Irfan Altas, Md. Islam. Exploring Timeline-Based Malware Classification. Lech J. Janczewski; Henry B. Wolfe; Sujeet Shenoi. 28th Security and Privacy Protection in Information Processing Systems (SEC), Jul 2013, Auckland, New Zealand. Springer, IFIP Advances in Information and Communication Technology, AICT-405, pp.1-13, 2013, Security and Privacy Protection in Information Processing Systems. 〈10.1007/978-3-642-39218-4_1〉. 〈hal-01463831〉

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