Picking on the family: disrupting android malware triage by forcing misclassification

Calleja, Alejandro, Martín, Alejandro, Menéndez, Héctor D. ORCID logoORCID: https://orcid.org/0000-0002-6314-3725, Tapiador, Juan and Clark, David (2018) Picking on the family: disrupting android malware triage by forcing misclassification. Expert Systems with Applications, 95 . pp. 113-126. ISSN 0957-4174 [Article] (doi:10.1016/j.eswa.2017.11.032)

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Machine learning classification algorithms are widely applied to different malware analysis problems because of their proven abilities to learn from examples and perform relatively well with little human input. Use cases include the labelling of malicious samples according to families during triage of suspected malware. However, automated algorithms are vulnerable to attacks. An attacker could carefully manipulate the sample to force the algorithm to produce a particular output. In this paper we discuss one such attack on Android malware classifiers. We design and implement a prototype tool, called IagoDroid, that takes as input a malware sample and a target family, and modifies the sample to cause it to be classified as belonging to this family while preserving its original semantics. Our technique relies on a search process that generates variants of the original sample without modifying their semantics. We tested IagoDroid against RevealDroid, a recent, open source, Android malware classifier based on a variety of static features. IagoDroid successfully forces misclassification for 28 of the 29 representative malware families present in the DREBIN dataset. Remarkably, it does so by modifying just a single feature of the original malware. On average, it finds the first evasive sample in the first search iteration, and converges to a 100% evasive population within 4 iterations. Finally, we introduce RevealDroid*, a more robust classifier that implements several techniques proposed in other adversarial learning domains. Our experiments suggest that RevealDroid* can correctly detect up to 99% of the variants generated by IagoDroid.

Item Type: Article
Keywords (uncontrolled): Malware classification, adversarial learning, genetic algorithms,Iagodroid
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 28796
Notes on copyright: © 2017 The Authors.
Published by Elsevier Ltd. This is an open access article under the CC BY license. (https://creativecommons.org/licenses/by/4.0/)
Useful Links:
Depositing User: Hector Menendez Benito
Date Deposited: 02 Feb 2020 20:22
Last Modified: 03 Nov 2020 10:55
URI: https://eprints.mdx.ac.uk/id/eprint/28796

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