{"title":"Can Monitoring System State + Counting Custom Instruction Sequences Aid Malware Detection?","authors":"Aditya Rohan, K. Basu, R. Karri","doi":"10.1109/ATS47505.2019.00007","DOIUrl":null,"url":null,"abstract":"Signature and behavior-based anti-virus systems (AVS) are traditionally used to detect Malware. However, these AVS fail to catch metamorphic and polymorphic Malware-which can reconstruct themselves every generation or every instance. We introduce two Machine learning (ML) approaches on system state + instruction sequences – which use hardware debug data – to detect such challenging Malware. Our experiments on hundreds of Intel Malware samples show that the techniques either alone or jointly detect Malware with ≥ 99.5% accuracy.","PeriodicalId":258824,"journal":{"name":"2019 IEEE 28th Asian Test Symposium (ATS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 28th Asian Test Symposium (ATS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATS47505.2019.00007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Signature and behavior-based anti-virus systems (AVS) are traditionally used to detect Malware. However, these AVS fail to catch metamorphic and polymorphic Malware-which can reconstruct themselves every generation or every instance. We introduce two Machine learning (ML) approaches on system state + instruction sequences – which use hardware debug data – to detect such challenging Malware. Our experiments on hundreds of Intel Malware samples show that the techniques either alone or jointly detect Malware with ≥ 99.5% accuracy.
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