2,287 research outputs found
Using HTML5 to Prevent Detection of Drive-by-Download Web Malware
The web is experiencing an explosive growth in the last years. New
technologies are introduced at a very fast-pace with the aim of narrowing the
gap between web-based applications and traditional desktop applications. The
results are web applications that look and feel almost like desktop
applications while retaining the advantages of being originated from the web.
However, these advancements come at a price. The same technologies used to
build responsive, pleasant and fully-featured web applications, can also be
used to write web malware able to escape detection systems. In this article we
present new obfuscation techniques, based on some of the features of the
upcoming HTML5 standard, which can be used to deceive malware detection
systems. The proposed techniques have been experimented on a reference set of
obfuscated malware. Our results show that the malware rewritten using our
obfuscation techniques go undetected while being analyzed by a large number of
detection systems. The same detection systems were able to correctly identify
the same malware in its original unobfuscated form. We also provide some hints
about how the existing malware detection systems can be modified in order to
cope with these new techniques.Comment: This is the pre-peer reviewed version of the article: \emph{Using
HTML5 to Prevent Detection of Drive-by-Download Web Malware}, which has been
published in final form at \url{http://dx.doi.org/10.1002/sec.1077}. This
article may be used for non-commercial purposes in accordance with Wiley
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Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Malware still constitutes a major threat in the cybersecurity landscape, also
due to the widespread use of infection vectors such as documents. These
infection vectors hide embedded malicious code to the victim users,
facilitating the use of social engineering techniques to infect their machines.
Research showed that machine-learning algorithms provide effective detection
mechanisms against such threats, but the existence of an arms race in
adversarial settings has recently challenged such systems. In this work, we
focus on malware embedded in PDF files as a representative case of such an arms
race. We start by providing a comprehensive taxonomy of the different
approaches used to generate PDF malware, and of the corresponding
learning-based detection systems. We then categorize threats specifically
targeted against learning-based PDF malware detectors, using a well-established
framework in the field of adversarial machine learning. This framework allows
us to categorize known vulnerabilities of learning-based PDF malware detectors
and to identify novel attacks that may threaten such systems, along with the
potential defense mechanisms that can mitigate the impact of such threats. We
conclude the paper by discussing how such findings highlight promising research
directions towards tackling the more general challenge of designing robust
malware detectors in adversarial settings
Adversarial Detection of Flash Malware: Limitations and Open Issues
During the past four years, Flash malware has become one of the most
insidious threats to detect, with almost 600 critical vulnerabilities targeting
Adobe Flash disclosed in the wild. Research has shown that machine learning can
be successfully used to detect Flash malware by leveraging static analysis to
extract information from the structure of the file or its bytecode. However,
the robustness of Flash malware detectors against well-crafted evasion attempts
- also known as adversarial examples - has never been investigated. In this
paper, we propose a security evaluation of a novel, representative Flash
detector that embeds a combination of the prominent, static features employed
by state-of-the-art tools. In particular, we discuss how to craft adversarial
Flash malware examples, showing that it suffices to manipulate the
corresponding source malware samples slightly to evade detection. We then
empirically demonstrate that popular defense techniques proposed to mitigate
evasion attempts, including re-training on adversarial examples, may not always
be sufficient to ensure robustness. 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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