PageRank in Malware Categorization

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

2 Citations (Scopus)
222 Downloads (Pure)

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.
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)
Pages291-295
Number of pages5
ISBN (Print)978-1-4503-3738-0
DOIs
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
https://sites.google.com/site/acmracs2015/home

Conference

ConferenceACM Research in Adaptive and Convergent Systems 2015
Abbreviated titleACM RACS
CountryCzech Republic
CityPrague
Period09/10/201512/10/2015
Internet address

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  • Cite this

    Kang, B., Yerima, S., McLaughlin, K., & Sezer, S. (2015). PageRank in Malware Categorization. In RACS: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems (pp. 291-295). Association for Computing Machinery (ACM). https://doi.org/10.1145/2811411.2811514