PageRank in Malware Categorization

BooJoong Kang; Suleiman Yerima; Kieran McLaughlin; Sakir Sezer

doi:10.1145/2811411.2811514

PageRank in Malware Categorization

BooJoong Kang, Suleiman Yerima, Kieran McLaughlin, Sakir Sezer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

223 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 language	English
Title of host publication	RACS: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems
Place of Publication	Czech Republic
Publisher	Association for Computing Machinery (ACM)
Pages	291-295
Number of pages	5
ISBN (Print)	978-1-4503-3738-0
DOIs	https://doi.org/10.1145/2811411.2811514
Publication status	Published - Oct 2015
Event	ACM Research in Adaptive and Convergent Systems 2015 - Czech Technical University, Prague, Czech Republic Duration: 09 Oct 2015 → 12 Oct 2015 Conference number: 2015 https://sites.google.com/site/acmracs2015/home

Conference

Conference	ACM Research in Adaptive and Convergent Systems 2015
Abbreviated title	ACM RACS
Country	Czech Republic
City	Prague
Period	09/10/2015 → 12/10/2015
Internet address	https://sites.google.com/site/acmracs2015/home

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10.1145/2811411.2811514

PageRank in Malware Categorization
"© 2015 BoojangKang et al. This is the authors' version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RACS: Proceedings of the 2015 Conference on research in adaptive and convergent systems . Publisher, http://dx.doi.org/10.1145/2811411.2811514
Accepted author manuscript, 618 KB

Cite this

@inproceedings{e18e274738024b8fae273ac06b3157fa,

title = "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.",

author = "BooJoong Kang and Suleiman Yerima and Kieran McLaughlin and Sakir Sezer",

year = "2015",

month = oct,

doi = "10.1145/2811411.2811514",

language = "English",

isbn = "978-1-4503-3738-0",

pages = "291--295",

booktitle = "RACS: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems",

publisher = "Association for Computing Machinery (ACM)",

note = "ACM Research in Adaptive and Convergent Systems 2015, ACM RACS ; Conference date: 09-10-2015 Through 12-10-2015",

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}

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. Association for Computing Machinery (ACM), Czech Republic, pp. 291-295, ACM Research in Adaptive and Convergent Systems 2015, Prague, Czech Republic, 09/10/2015. https://doi.org/10.1145/2811411.2811514

TY - GEN

T1 - PageRank in Malware Categorization

AU - Kang, BooJoong

AU - Yerima, Suleiman

AU - McLaughlin, Kieran

AU - Sezer, Sakir

N1 - Conference code: 2015

PY - 2015/10

Y1 - 2015/10

N2 - 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.

AB - 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.

UR - https://sites.google.com/site/acmracs2015/home

U2 - 10.1145/2811411.2811514

DO - 10.1145/2811411.2811514

M3 - Conference contribution

SN - 978-1-4503-3738-0

SP - 291

EP - 295

BT - RACS: Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems

PB - Association for Computing Machinery (ACM)

CY - Czech Republic

T2 - ACM Research in Adaptive and Convergent Systems 2015

Y2 - 9 October 2015 through 12 October 2015

ER -

PageRank in Malware Categorization

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