Adversarial Detection of Flash Malware: Limitations and Open Issues
By Davide Maiorca, Battista Biggio, Maria Elena Chiappe, Giorgio Giacinto. 2017
27 citations recorded by the source corpus.
Abstract 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 Player 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.
Subfields: Artificial Intelligence, Information Systems, Privacy & Security, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Ambra Demontis
- Claudia Eckert
- Giorgio Fumera
- Bojan Kolosnjaji
In Information Systems
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Claudia Eckert
- Ambra Demontis
- Giorgio Fumera
- Joseph Gardiner
In Privacy & Security
- Battista Biggio
- Fabio Roli
- Davide Maiorca
- Giorgio Giacinto
- Claudia Eckert
- Ambra Demontis
- Bojan Kolosnjaji
- Giorgio Fumera
In Signal Processing
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Ambra Demontis
- Giorgio Fumera
- Claudia Eckert
- Bojan Kolosnjaji
Citations in the corpus
- Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables (2018, background): “To our knowledge, this is among the first attacks proposed at the byte-level scale, similarly to [14], as most work in adversarial machine learning for malware detection has considered injection and removal of API calls or similar characteristics [3], [6], [9], [11], [12], [16], [24], [27], [28].”