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2017 | OriginalPaper | Buchkapitel

SecMD: Make Machine Learning More Secure Against Adversarial Malware Attacks

verfasst von : Lingwei Chen, Yanfang Ye

Erschienen in: AI 2017: Advances in Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

As machine learning based systems have been successfully deployed for malware detection, the incentive for defeating them increases. In this paper, we explore the security of machine learning in malware detection on the basis of a learning-based classifier. In particular, (1) considering different capabilities of the attackers (i.e., how much knowledge they have regarding feature representation, training set, and learning algorithm), we present a set of corresponding adversarial attacks and implement a general attack model AdvAttack to thoroughly assess the adversary behaviors; (2) to effectively counter these evasion attacks, we propose a resilient yet elegant secure-learning paradigm SecMD to improve the system security against a wide class of adversarial attacks. Promising experimental results based on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed methods.

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Titel: SecMD: Make Machine Learning More Secure Against Adversarial Malware Attacks
verfasst von: Lingwei Chen
Yanfang Ye
Verlag: Springer International Publishing
Buch: AI 2017: Advances in Artificial Intelligence
Print ISBN: 978-3-319-63003-8

Electronic ISBN: 978-3-319-63004-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-63004-5_7

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