A study on similarity calculation method for API invocation sequences
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shim, Yu Jin | - |
dc.contributor.author | Kim, Tae Guen | - |
dc.contributor.author | Im, Eul Gyu | - |
dc.date.accessioned | 2022-07-15T20:26:24Z | - |
dc.date.available | 2022-07-15T20:26:24Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2015-11 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/156040 | - |
dc.description.abstract | Malware variants have been developed and spread in the Internet, and the number of new malware variants is increases every year. Recently, malware is applied with obfuscation and mutation techniques to hide its existence, and malware variants are developed with various automatic tools that transform the properties of existing malware to avoid static analysis based malware detection systems. It is difficult to detect such obfuscated malware with static-based signatures, so we have designed a detection system based on dynamic analysis. In this paper, we propose a dynamic analysis based system that uses the API invocation sequences to compare behaviors of suspicious software with behaviors of existing malware. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | A study on similarity calculation method for API invocation sequences | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Im, Eul Gyu | - |
dc.identifier.doi | 10.1007/978-3-319-25754-9_43 | - |
dc.identifier.scopusid | 2-s2.0-84952360257 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.9436, pp.492 - 501 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 9436 | - |
dc.citation.startPage | 492 | - |
dc.citation.endPage | 501 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Calculations | - |
dc.subject.keywordPlus | Computer crime | - |
dc.subject.keywordPlus | Dynamic analysis | - |
dc.subject.keywordPlus | Malware | - |
dc.subject.keywordPlus | Rough set theory | - |
dc.subject.keywordPlus | API invocation sequence | - |
dc.subject.keywordPlus | Automatic tools | - |
dc.subject.keywordPlus | Detection system | - |
dc.subject.keywordPlus | Malware detection | - |
dc.subject.keywordPlus | Similarity calculation | - |
dc.subject.keywordPlus | Static analysis | - |
dc.subject.keywordAuthor | API invocation sequence | - |
dc.subject.keywordAuthor | Dynamic analysis | - |
dc.subject.keywordAuthor | Malware detection | - |
dc.subject.keywordAuthor | Similarity calculation method | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-319-25754-9_43 | - |
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