Giovanni Lagorio

Luca Demetrio, Scott E. Coull, Battista Biggio et al.

Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples in this paper - can bypass machine learning-based detection relying on static code analysis by perturbing relatively few input bytes. To preserve malicious functionality, previous attacks either add bytes to existing non-functional areas of the file, potentially limiting their effectiveness, or require running computationally-demanding validation steps to discard malware variants that do not correctly execute in sandbox environments. In this work, we overcome these limitations by developing a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks based on practical, functionality-preserving manipulations to the Windows Portable Executable (PE) file format. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section. Our experimental results show that these attacks outperform existing ones in both white-box and black-box scenarios, achieving a better trade-off in terms of evasion rate and size of the injected payload, while also enabling evasion of models that have been shown to be robust to previous attacks. To facilitate reproducibility of our findings, we open source our framework and all the corresponding attack implementations as part of the secml-malware Python library. We conclude this work by discussing the limitations of current machine learning-based malware detectors, along with potential mitigation strategies based on embedding domain knowledge coming from subject-matter experts directly into the learning process.

Lorenzo Maffia, Dario Nisi, Platon Kotzias et al.

By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of different evasion techniques have been documented over the years, and PoC implementations have been collected in public frameworks, like the popular Al-Khaser. As malware authors tend to reuse existing approaches, it is common to observe the same evasive techniques in malware samples of different families. However, no measurement study has been conducted to date to assess the adoption and prevalence of evasion techniques. In this paper, we present a large-scale study, conducted by dynamically analyzing more than 180K Windows malware samples, on the evolution of evasive techniques over the years. To perform the experiments, we developed a custom Pin-based Evasive Program Profiler (Pepper), a tool capable of both detecting and circumventing 53 anti-dynamic-analysis techniques of different categories, ranging from anti-debug to virtual machine detection. To observe the phenomenon of evasion from different points of view, we employed four different datasets, including benign files, advanced persistent threat (APTs), malware samples collected over a period of five years, and a recent collection of different families submitted to VirusTotal over a one-month period.

Luca Demetrio, Andrea Valenza, Gabriele Costa et al.

Web Application Firewalls are widely used in production environments to mitigate security threats like SQL injections. Many industrial products rely on signature-based techniques, but machine learning approaches are becoming more and more popular. The main goal of an adversary is to craft semantically malicious payloads to bypass the syntactic analysis performed by a WAF. In this paper, we present WAF-A-MoLE, a tool that models the presence of an adversary. This tool leverages on a set of mutation operators that alter the syntax of a payload without affecting the original semantics. We evaluate the performance of the tool against existing WAFs, that we trained using our publicly available SQL query dataset. We show that WAF-A-MoLE bypasses all the considered machine learning based WAFs.

Luca Demetrio, Battista Biggio, Giovanni Lagorio et al.

Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes.