1 citations · 2 across the 11 of their papers we have counts for
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Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation
Luca Scionis, Luca Melis, Maura Pintor +5
Adversarial robustness is commonly evaluated with predefined attack ensembles, such as AutoAttack, at a single perturbation budget and on a selective choice of pertur…
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…
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Srishti Gupta, Zhang Chen, Luca Demetrio +9
Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is consi…
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…
Out-of-Distribution Detection for Continual Learning: Design Principles and Benchmarking
Srishti Gupta, Riccardo Balia, Daniele Angioni +7
Recent years have witnessed significant progress in the development of machine learning models across a wide range of fields, fueled by increased computational resources, large-sca…