A Human-in-the-Loop Approach to Malware Author Classification
- Authors
- Kim, Eujeanne; Park, Sung-Jun; Chae, Dong-Kyu; Choi, Seokwoo; Kim, Sang-Wook
- Issue Date
- Oct-2020
- Publisher
- Association for Computing Machinery
- Keywords
- human-in-the-loop approach; malware author groups; malware classification
- Citation
- International Conference on Information and Knowledge Management, Proceedings, pp.3289 - 3292
- Indexed
- SCOPUS
- Journal Title
- International Conference on Information and Knowledge Management, Proceedings
- Start Page
- 3289
- End Page
- 3292
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144954
- DOI
- 10.1145/3340531.3417467
- Abstract
- For these few decades malwares have been posing a major concern in the cyber security. Recently, a number of "author groups" have been generating lots of newmalwares by sharing source code within a group and exploiting evasive schemes such as polymorphism and metamorphism. This motivates us to study the problem of identifying the author group of a given malware, which would be able to work for not only blocking malwares but also legally punishing suspected malware authors. In this paper, we propose a human-machine collaborative approach for classifying author groups of malwares accurately. We also propose a visualization method for helping human experts to make the decision easily. We verify the superiority of our framework through extensive experiments using real-world malware data.
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