PageRank in Malware Categorization
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.
Keywords
Cite
@article{arxiv.1608.00866,
title = {PageRank in Malware Categorization},
author = {BooJoong Kang and Suleiman Y. Yerima and Kieran McLaughlin and Sakir Sezer},
journal= {arXiv preprint arXiv:1608.00866},
year = {2016}
}
Comments
In RACS:Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems. (pp. 291-295). Czech Republic: Association for Computing Machinery (ACM)