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Journal Article Improvement of Malware Detection and Classification using API Call Sequence Alignment and Visualization
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Authors
Hyunjoo Kim, Jonghyun Kim, Youngsoo Kim, Ikkyun Kim, Kuinam J. Kim, Hyuncheol Kim
Issue Date
2019-01
Citation
Cluster Computing, v.22, no.Supp.1, pp.921-929
ISSN
1386-7857
Publisher
Springer
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1007/s10586-017-1110-2
Project Code
17HH1900, Cloud based Security Intelligence Technology Development for the Customized Security Service Provisioning, Kim Jonghyun
Abstract
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.
KSP Keywords
API call sequence, Anti-Virtualization, Anti-emulation, Classification method, Detection technology, Dynamic analysis, Encapsulation technology, Malware detection, Rapid Response, Sequence alignment algorithm, detect malware