7 papers
Regression-aware Continual Learning for Android Malware Detection
Daniele Ghiani, Daniele Angioni, Giorgio Piras +6
Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated. With antivirus vendors processing hundreds of thousands of new samples daily, datasets…
DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors
Christian Scano, Diego Soi, Angelo Sotgiu +5
Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that, despite claims, existing prob…
Label-efficient Training Updates for Malware Detection over Time
Luca Minnei, Cristian Manca, Giorgio Piras +6
Machine Learning (ML)-based detectors are becoming essential to counter the proliferation of malware. However, common ML algorithms are not designed to cope with the dynamic nature…
Evaluating Line-level Localization Ability of Learning-based Code Vulnerability Detection Models
Marco Pintore, Giorgio Piras, Angelo Sotgiu +2
To address the extremely concerning problem of software vulnerability, system security is often entrusted to Machine Learning (ML) algorithms. Despite their now established detecti…
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
Hicham Eddoubi, Jonas Ricker, Federico Cocchi +7
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinf…
Robust image classification with multi-modal large language models
Francesco Villani, Igor Maljkovic, Dario Lazzaro +3
Deep Neural Networks are vulnerable to adversarial examples, i.e., carefully crafted input samples that can cause models to make incorrect predictions with high confidence. To miti…