10 citations · 20 across the 14 of their papers we have counts for
16 papers
Lipschitz Thinking: Ten Years of Certifiable-by-Design Robust Neural Networks
Fabio Brau, Giorgio Piras, Maura Pintor +1
The Lipschitz property of a deep neural network provides a direct measure of its sensitivity to input perturbations and, when explicitly controlled, offers a principled way to limi…
Latent-space Attacks for Refusal Evasion in Language Models
Giorgio Piras, Raffaele Mura, Fabio Brau +4
Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations. Existing methods do so by…
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…
SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Cristian Manca, Christian Scano, Giorgio Piras +3
Machine learning-based anomaly detection systems are increasingly being adopted in 5G Core networks to monitor complex, high-volume traffic. However, most existing approaches are e…
SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models
Giorgio Piras, Raffaele Mura, Fabio Brau +3
Refusal refers to the functional behavior enabling safety-aligned language models to reject harmful or unethical prompts. Following the growing scientific interest in mechanistic i…
LatentBreak: Jailbreaking Large Language Models through Latent Space Feedback
Raffaele Mura, Giorgio Piras, Kamilė Lukošiūtė +3
Jailbreaks are adversarial attacks designed to bypass the built-in safety mechanisms of large language models. Automated jailbreaks typically optimize an adversarial suffix or adap…
S2AP: Score-space Sharpness Minimization for Adversarial Pruning
Giorgio Piras, Qi Zhao, Fabio Brau +3
Adversarial pruning methods have emerged as a powerful tool for compressing neural networks while preserving robustness against adversarial attacks. These methods typically follow…
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…
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…
On the Robustness of Adversarial Training Against Uncertainty Attacks
Emanuele Ledda, Giovanni Scodeller, Daniele Angioni +5
In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of i…
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
Giorgio Piras, Maura Pintor, Ambra Demontis +3
Recent work has proposed neural network pruning techniques to reduce the size of a network while preserving robustness against adversarial examples, i.e., well-crafted inputs induc…
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks
Raffaele Mura, Giuseppe Floris, Luca Scionis +6
Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed l…
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Giuseppe Floris, Raffaele Mura, Luca Scionis +4
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging. In this work, we show that hyperparameter optimization can improve fas…
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks
Giorgio Piras, Maura Pintor, Ambra Demontis +1
Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks…
Adversarial Attacks Against Uncertainty Quantification
Emanuele Ledda, Daniele Angioni, Giorgio Piras +3
Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantifi…
Explaining Machine Learning DGA Detectors from DNS Traffic Data
Giorgio Piras, Maura Pintor, Luca Demetrio +1
One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of…