PageRank in Malware Categorization

BooJoong Kang, Suleiman Yerima, Kieran McLaughlin, Sakir Sezer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
223 Downloads (Pure)


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 languageEnglish
Title of host publicationRACS: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems
Place of PublicationCzech Republic
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Print)978-1-4503-3738-0
Publication statusPublished - Oct 2015
EventACM Research in Adaptive and Convergent Systems 2015 - Czech Technical University, Prague, Czech Republic
Duration: 09 Oct 201512 Oct 2015
Conference number: 2015


ConferenceACM Research in Adaptive and Convergent Systems 2015
Abbreviated titleACM RACS
CountryCzech Republic
Internet address


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