6 papers · 1 filter
EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability
Andrea Ponte, Daniel Gibert, Matous Kozak +5
Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations (i) differ i…
ByteShield: Adversarially Robust End-to-End Malware Detection through Byte Masking
Daniel Gibert, Felip ManyÃ
Research has proven that end-to-end malware detectors are vulnerable to adversarial attacks. In response, the research community has proposed defenses based on randomized and (de)r…
DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
Daniel Pulido-Cortázar, Daniel Gibert, Felip ManyÃ
Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examp…
Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems
Daniel Gibert, Nikolaos Totosis, Constantinos Patsakis +2
The proliferation of malware, particularly through the use of packing, presents a significant challenge to static analysis and signature-based malware detection techniques. The app…
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
Daniel Gibert, Luca Demetrio, Giulio Zizzo +3
Deep learning-based malware detection systems are vulnerable to adversarial EXEmples - carefully-crafted malicious programs that evade detection with minimal perturbation. As such,…
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
Daniel Gibert
In this chapter, readers will explore how machine learning has been applied to build malware detection systems designed for the Windows operating system. This chapter starts by int…