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학술지 Improvement of Malware Detection and Classification using API Call Sequence Alignment and Visualization
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저자
김현주, 김종현, 김영수, 김익균, 김귀남, 김현철
발행일
201901
출처
Cluster Computing, v.22 no.Supp.1, pp.921-929
ISSN
1386-7857
출판사
Springer
DOI
https://dx.doi.org/10.1007/s10586-017-1110-2
협약과제
17HH1900, 맞춤형 보안서비스 제공을 위한 클라우드 기반 지능형 보안 기술 개발, 김종현
초록
Conventional malware detection technologies have the limitation to detect malware because recent malware uses a variety of the avoidance techniques such as obfuscation, packing, anti-virtualization, anti-emulation, encapsulation technology in order to evade the detection of malware. To overcome this limitation, it is necessary to obtain new detection technology which is able to quickly analyze massive malware and its variants, and take the rapid response to cyber intrusion. Therefore in this paper, we proposed the malware detection and classification method and implementation of our system based on the dynamic analysis using the behavioral sequence of malware (API call sequence) and sequence alignment algorithm (MSA). Also we evaluated the effectiveness of our proposed method through the experiment.
키워드
Behavioral sequence, Malware detection and classification, Multiple sequence alignment, Similarity, Visualization
KSP 제안 키워드
API call sequence, Anti-Virtualization, Anti-emulation, Classification method, Detection technology, Dynamic analysis, Encapsulation technology, Malware detection, Multiple sequence alignment(MSA), Rapid Response, Sequence alignment algorithm