text: 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.
  @cite_30: Adobe Flash is present on nearly every PC, and it is increasingly being targeted by malware authors. Despite this, research into methods for detecting malicious Flash files has been limited. Similarly, there is very little documentation available about the techniques commonly used by Flash malware. Instead, most research has focused on JavaScript malware. This paper discusses common techniques such as heap spraying, JIT spraying, and type confusion exploitation in the context of Flash malware. Where applicable, these techniques are compared to those used in malicious JavaScript. Subsequently, FlashDetect is presented, an offline Flash file analyzer that uses both dynamic and static analysis, and that can detect malicious Flash files using ActionScript 3. FlashDetect classifies submitted files using a naive Bayesian classifier based on a set of predefined features. Our experiments show that FlashDetect has high classification accuracy, and that its efficacy is comparable with that of commercial anti-virus products.
summary: As -based malicious attacks started to considerably grow in @math , the number of detection approaches is rather limited. FlashDetect @cite_30 is one of the first approaches to the detection of @math -based malware. The authors instrumented Lightspark , an open source viewer, to perform dynamic analysis of malicious files. From this analysis, the system extracts features such as the number of ByteArray -related method calls, the presence of the loadBytes method, and so forth. FlashDetect was employed inside the Wepawet service, which is sadly not available anymore.