PageRank in Malware Categorization
This addresses malware detection for cybersecurity applications, but it is incremental as it applies existing PageRank methods to a new domain.
The paper tackles malware categorization by modeling malware behavior through instruction sequences using PageRank algorithms to compute ranks as features for machine learning, achieving improved categorization accuracy with bagging and boosting techniques.
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.