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공공누리This item is licensed Korea Open Government License

dc.contributor.author
Dimitrios Damopoulos
dc.contributor.author
Georgios Kambourakis
dc.contributor.author
Stefanos Gritzalis
dc.contributor.author
박상오
dc.date.accessioned
2019-08-28T07:41:36Z
dc.date.available
2019-08-28T07:41:36Z
dc.date.issued
2014-12-04
dc.identifier.issn
1936-6442
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/14341
dc.identifier.uri
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART69962871
dc.description.abstract
It is without a doubt that malware especially designed for modern mobile platforms is rapidly becoming a serious threat. The problem is further multiplexed by thegrowing convergence of wired, wireless and cellular networks, since virus writers can now develop sophisticated malicious software that is able to migrate across network domains. This is done in an effort to exploit vulnerabilities and services specific to each network. So far, research in dealing with this risk has concentrated on the Android platform and mainly considered static solutions rather than dynamic ones. Compelled by this fact, in this paper, we contributea fully-fledged tool able to dynamically analyze any iOS software in terms of method invocation (i.e., which API methods the application invokes and under what order), and produce exploitable results that can be used to manually or automatically trace software’s behavior to decide if it contains malicious code or not. By employing real life malware we assessed our tool both manually, as well as, via heuristic techniques and the results we obtained seem highly accurate in detecting malicious code
dc.language
eng
dc.relation.ispartofseries
Peer-to-Peer Networking and Applications
dc.title
Exposing mobile malware from the inside (or what is your mobile app really doing?)
dc.citation.endPage
697
dc.citation.number
4
dc.citation.startPage
687
dc.citation.volume
7
dc.subject.keyword
Malware
dc.subject.keyword
iOS
dc.subject.keyword
Dynamic analysis
dc.subject.keyword
Behavior-based detection
dc.subject.keyword
Smartphone
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7. KISTI 연구성과 > 학술지 발표논문
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