  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.
