NDLI: Poisoning behavioral malware clustering

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Poisoning behavioral malware clustering

Content Provider	ACM Digital Library
Author	Giacinto, Giorgio Roli, Fabio Biggio, Battista Ariu, Davide Wressnegger, Christian Rieck, Konrad Corona, Igino
Abstract	Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems. However, the suitability of clustering algorithms for security-sensitive settings has been recently questioned by showing that they can be significantly compromised if an attacker can exercise some control over the input data. In this paper, we revisit this problem by focusing on behavioral malware clustering approaches, and investigate whether and to what extent an attacker may be able to subvert these approaches through a careful injection of samples with poisoning behavior. To this end, we present a case study on Malheur, an open-source tool for behavioral malware clustering. Our experiments not only demonstrate that this tool is vulnerable to poisoning attacks, but also that it can be significantly compromised even if the attacker can only inject a very small percentage of attacks into the input data. As a remedy, we discuss possible countermeasures and highlight the need for more secure clustering algorithms.
Starting Page	27
Ending Page	36
Page Count	10
File Format	PDF
ISBN	9781450331531
DOI	10.1145/2666652.2666666
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Security evaluation Adversarial machine learning Clustering Computer security Malware detection Unsupervised learning
Content Type	Text
Resource Type	Article

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