Adversarial Detection of Flash Malware: Limitations and Open Issues
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During the past two years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash Player disclosed in the wild. Research has shown that machine learning can be successfully used to tackle this increasing variability and sophistication of Flash malware, by simply leveraging static analysis to extract information from the structure of the file or from its bytecode. However, the robustness of such systems against well-crafted evasion attempts - also known as adversarial examples - has never been investigated. In this paper, we first discuss how to craft adversarial Flash malware examples, and show that it suffices to only slightly manipulate them to evade detection. We then empirically demonstrate that popular defense techniques proposed to mitigate such threat, including re-training on adversarial examples, may not always be effective. We argue that this occurs when the feature vectors extracted from adversarial examples become indistinguishable from those of benign data, meaning that the given feature representation is intrinsically vulnerable. In this respect, we are the first to formally define and quantitatively characterize this vulnerability, highlighting when an attack can be countered by solely improving the security of the learning algorithm, or when it requires also considering additional features. We conclude the paper by suggesting alternative research directions to improve the security of learning-based Flash malware detectors.
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