PageRank in Malware Categorization
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.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Kang, B., Yerima, S. Y., McLaughlin, K., Sezer, S. (2015) PageRank in Malware Categorization. In: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems (RACS), New York: ACM.
ISBN : 9781450337380
Research Group : Cyber Technology Institute (CTI)
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes