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Accepted Paper

The HO-FMN paper has been accepted in the Neurocomputing journal!

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publications

Explaining Machine Learning DGA Detectors from DNS Traffic Data

Published in CEUR Workshop Proceedings, Vol. 3260, pp. 150-168, 2022

This paper discusses methods to explain machine learning-based Domain Generation Algorithm (DGA) detectors using DNS traffic data.

Recommended citation: Piras, G., Pintor, M., Demetrio, L., & Biggio, B. (2022). "Explaining Machine Learning DGA Detectors from DNS Traffic Data." CEUR Workshop Proceedings, 3260, 150-168.
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Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

Published in ESANN-23, 2023

This paper presents enhancements to fast minimum-norm adversarial attacks through hyperparameter optimization techniques.

Recommended citation: Floris, G., Mura, R., Scionis, L., Piras, G., Pintor, M., Demontis, A., & Biggio, B. (2023). "Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization." arXiv preprint arXiv:2310.08177.
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Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks

Published in 2023 International Conference on Machine Learning and Cybernetics (ICMLC), 2023

This research re-evaluates the effectiveness of adversarial pruning techniques in neural networks.

Recommended citation: Piras, G., Pintor, M., Demontis, A., & Biggio, B. (2023). "Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks." 2023 International Conference on Machine Learning and Cybernetics (ICMLC).
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Adversarial Attacks Against Uncertainty Quantification

Published in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

This study explores adversarial attacks targeting uncertainty quantification methods in machine learning models.

Recommended citation: Ledda, E., Angioni, D., Piras, G., Fumera, G., Biggio, B., & Roli, F. (2023). "Adversarial Attacks Against Uncertainty Quantification." Proceedings of the IEEE/CVF International Conference on Computer Vision.
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Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness

Published in arXiv preprint arXiv:2409.01249, 2024

This survey provides a comprehensive overview and benchmark of pruning methods aimed at enhancing adversarial robustness in neural networks.

Recommended citation: Piras, G., Pintor, M., Demontis, A., Biggio, B., Giacinto, G., & Roli, F. (2024). "Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness."
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On the Robustness of Adversarial Training Against Uncertainty Attacks

Published in arXiv preprint arXiv:2410.21952, 2024

This paper examines the robustness of adversarial training methods against uncertainty-based attacks.

Recommended citation: Ledda, E., Scodeller, G., Angioni, D., Piras, G., Cinà, A. E., Fumera, G., Biggio, B., & Roli, F. (2024). "On the Robustness of Adversarial Training Against Uncertainty Attacks." arXiv preprint arXiv:2410.21952.
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HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks

Published in Neurocomputing, Vol. 616, Article 128918, 2025

This study introduces HO-FMN, a method for hyperparameter optimization in fast minimum-norm adversarial attacks.

Recommended citation: Mura, R., Floris, G., Scionis, L., Piras, G., Pintor, M., Demontis, A., Giacinto, G., & Biggio, B. (2025). "HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks." Neurocomputing, 616, 128918.
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Giorgio Piras