Abstract
In this paper, we propose a malware categorization method that models malware behavior in terms of instructions using PageRank. PageRank computes ranks of web pages based on structural information and can also compute ranks of instructions that represent the structural information of the instructions in malware analysis methods. Our malware categorization method uses the computed ranks as features in machine learning algorithms. In the evaluation, we compare the effectiveness of different PageRank algorithms and also investigate bagging and boosting algorithms to improve the categorization accuracy.
| Original language | English |
|---|---|
| Title of host publication | RACS: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems |
| Place of Publication | Czech Republic |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 291-295 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-4503-3738-0 |
| DOIs | |
| Publication status | Published - Oct 2015 |
| Event | ACM Research in Adaptive and Convergent Systems 2015 - Czech Technical University, Prague, Czech Republic Duration: 09 Oct 2015 → 12 Oct 2015 Conference number: 2015 https://sites.google.com/site/acmracs2015/home |
Conference
| Conference | ACM Research in Adaptive and Convergent Systems 2015 |
|---|---|
| Abbreviated title | ACM RACS |
| Country | Czech Republic |
| City | Prague |
| Period | 09/10/2015 → 12/10/2015 |
| Internet address |
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Cite this
Kang, B., Yerima, S., McLaughlin, K., & Sezer, S. (2015). PageRank in Malware Categorization. In RACS: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems (pp. 291-295). Association for Computing Machinery (ACM). https://doi.org/10.1145/2811411.2811514