11,174 research outputs found
Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks
Deep learning has been rapidly employed in many applications revolutionizing
many industries, but it is known to be vulnerable to adversarial attacks. Such
attacks pose a serious threat to deep learning-based systems compromising their
integrity, reliability, and trust. Interpretable Deep Learning Systems (IDLSes)
are designed to make the system more transparent and explainable, but they are
also shown to be susceptible to attacks. In this work, we propose a novel
microbial genetic algorithm-based black-box attack against IDLSes that requires
no prior knowledge of the target model and its interpretation model. The
proposed attack is a query-efficient approach that combines transfer-based and
score-based methods, making it a powerful tool to unveil IDLS vulnerabilities.
Our experiments of the attack show high attack success rates using adversarial
examples with attribution maps that are highly similar to those of benign
samples which makes it difficult to detect even by human analysts. Our results
highlight the need for improved IDLS security to ensure their practical
reliability.Comment: arXiv admin note: text overlap with arXiv:2307.0649
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201
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
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