Fast malware family detection method using control flow graphs
- Authors
- Kang, Boojoong; Kim, Hye Seon; Kim, T.; Kwon, H.; Im, E.G.
- Issue Date
- Nov-2011
- Publisher
- Association for Computing Machinary, Inc.
- Keywords
- Bloom filter; control flow graph; malware analysis; network security
- Citation
- Proceedings of the 2011 ACM Research in Applied Computation Symposium, RACS 2011, pp.287 - 292
- Indexed
- SCOPUS
- Journal Title
- Proceedings of the 2011 ACM Research in Applied Computation Symposium, RACS 2011
- Start Page
- 287
- End Page
- 292
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/167282
- DOI
- 10.1145/2103380.2103439
- ISSN
- 0000-0000
- Abstract
- As attackers make variants of existing malware, it is possible to detect unknown malware by comparing with already-known malware's information. Control flow graphs have been used in dynamic analysis of program source code. In this paper, we proposed a new method which can analyze and detect malware binaries using control flow graphs and Bloom filter by abstracting common characteristics of malware families. The experimental results showed that processing overhead of our proposed method is much lower than n-gram based methods.
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