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Malware Detection Using Rough Set Based Evolutionary Optimization

Abstract : Despite the existing anti-malware techniques and their interesting achieved results to "hook" attacks, the unstoppable evolution of malware makes the need for more capable malware detection systems overriding. In this paper, we propose a new malware detection technique named Bilevel-Roughset based Malware Detection (BLRDetect) that is based on, and exploits the benefits of, Bilevel optimization and Rough Set Theory. The upper-level of the Bilevel optimization component uses a Genetic Programming Algorithm in its chase of generating powerful detection rules while the lower-level leans on both a Genetic Algorithm and a Rough-Set module to produce high quality, and reliable, malware samples that escape, to their best, the upper-level's generated detection rules. Both levels interact with each other in a competitive way in order to produce populations that depend on one another. Our detection technique has proven its outperformance when tested against various stateof-the-art malware detection systems using common evaluation metrics.
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https://hal.uvsq.fr/hal-03495773
Contributor : Zaineb Chelly Dagdia Connect in order to contact the contributor
Submitted on : Monday, December 20, 2021 - 11:57:05 AM
Last modification on : Thursday, December 23, 2021 - 3:37:28 AM

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Manel Jerbi, Zaineb Chelly Dagdia, Slim Bechikh, Lamjed Ben Said. Malware Detection Using Rough Set Based Evolutionary Optimization. International Conference on Neural Information Processing, Dec 2021, Bali, Indonesia. pp.634-641, ⟨10.1007/978-3-030-92307-5_74⟩. ⟨hal-03495773⟩

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