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Malware categorization using dynamic mnemonic frequency analysis with redundancy filtering

Title
Malware categorization using dynamic mnemonic frequency analysis with redundancy filtering
Author
임을규
Keywords
Malware analysis; Dynamic analysis; Malware categorization; Mnemonic frequency; Redundancy filtering
Issue Date
2014-12
Publisher
Elsevier SCI Ltd
Citation
DIGITAL INVESTIGATION, 2014, 11(4), P.323-335
Abstract
The battle between malware developers and security analysts continues, and the number of malware and malware variants keeps increasing every year. Automated malware generation tools and various detection evasion techniques are also developed every year. To catch up with the advance of malware development technologies, malware analysis techniques need to be advanced to help security analysts. In this paper, we propose a malware analysis method to categorize malware using dynamic mnemonic frequencies. We also proposed a redundancy filtering technique to alleviate drawbacks of dynamic analysis. Experimental results show that our proposed method can categorize malware and can reduce storage overheads of dynamic analysis. (C) 2014 Elsevier Ltd. All rights reserved
URI
https://www.sciencedirect.com/science/article/pii/S1742287614000772?via%3Dihubhttp://hdl.handle.net/20.500.11754/54456
ISSN
1742-2876; 1873-202X
DOI
10.1016/j.diin.2014.06.003
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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