Similarity calculation method for user-define functions to detect malware variants
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Taeguen | - |
dc.contributor.author | Park, Jung Bin | - |
dc.contributor.author | Cho, In Gyeom | - |
dc.contributor.author | Im, Eul Gyu | - |
dc.contributor.author | Kang, Boojoong | - |
dc.contributor.author | Kang, Sooyong | - |
dc.date.accessioned | 2022-07-16T02:38:25Z | - |
dc.date.available | 2022-07-16T02:38:25Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2014-10 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/158945 | - |
dc.description.abstract | The number of malware has sharply increased over years, and it caused various damages on computing systems and data. In this paper, we propose techniques to detect malware variants. Malware authors usually reuse malware modules when they generate new malware or malware variants. Therefore, malware variants have common code for some functions in their binary files. We focused on this common code in this research, and proposed the techniques to detect malware variants through similarity calculation of user-defined function. Since many malware variants evade malware detection system by transforming their static signatures, to cope with this problem, we applied pattern matching algorithms for DNA variations in Bioinformatics to similarity calculation of malware binary files. Since the pattern matching algorithm we used provides the local alignment function, small modification of functions can be overcome. Experimental results show that our proposed method can detect malware similarity and it is more resilient than other methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Similarity calculation method for user-define functions to detect malware variants | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Im, Eul Gyu | - |
dc.identifier.doi | 10.1145/2663761.2664222 | - |
dc.identifier.scopusid | 2-s2.0-84910009144 | - |
dc.identifier.bibliographicCitation | Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014, pp.236 - 241 | - |
dc.relation.isPartOf | Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014 | - |
dc.citation.title | Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014 | - |
dc.citation.startPage | 236 | - |
dc.citation.endPage | 241 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Bioinformatics | - |
dc.subject.keywordPlus | Calculations | - |
dc.subject.keywordPlus | Computer crime | - |
dc.subject.keywordPlus | Malware | - |
dc.subject.keywordPlus | Pattern matching | - |
dc.subject.keywordPlus | Computing system | - |
dc.subject.keywordPlus | Malware analysis | - |
dc.subject.keywordPlus | Malware detection | - |
dc.subject.keywordPlus | Pattern matching algorithms | - |
dc.subject.keywordPlus | Similarity calculation | - |
dc.subject.keywordPlus | Smith-Waterman algorithm | - |
dc.subject.keywordPlus | Static signatures | - |
dc.subject.keywordPlus | User Defined Functions | - |
dc.subject.keywordPlus | Static analysis | - |
dc.subject.keywordAuthor | Malware analysis | - |
dc.subject.keywordAuthor | Smith-Waterman algorithm | - |
dc.subject.keywordAuthor | Static analysis | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/2663761.2664222 | - |
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