NDLI: Scalable malware clustering through coarse-grained behavior modeling

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Scalable malware clustering through coarse-grained behavior modeling

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Scalable malware clustering through coarse-grained behavior modeling

Content Provider	ACM Digital Library
Author	Tan, Hee Beng Kuan Shar, Lwin Khin Chandramohan, Mahinthan
Abstract	Anti-malware vendors receive several thousand new malware (malicious software) variants per day. Due to large volume of malware samples, it has become extremely important to group them based on their malicious characteristics. Grouping of malware variants that exhibit similar behavior helps to generate malware signatures more efficiently. Unfortunately, exponential growth of new malware variants and huge-dimensional feature space, as used in existing approaches, make the clustering task very challenging and difficult to scale. Furthermore, malware behavior modeling techniques proposed in the literature do not scale well, where malware feature space grows in proportion with the number of samples under examination. In this paper, we propose a scalable malware behavior modeling technique that models the interactions between malware and sensitive system resources in a coarse-grained manner. Coarse-grained behavior modeling enables us to generate malware feature space that does not grow in proportion with the number of samples under examination. A preliminary study shows that our approach generates 289 times less malware features and yet improves the average clustering accuracy by 6.20% comparing to a state-of-the-art malware clustering technique.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450316149
DOI	10.1145/2393596.2393627
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Coarse-grained behavior modeling Malware behavior modeling Malware clustering
Content Type	Text
Resource Type	Article

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