Zobrazeno 1 - 10
of 248
pro vyhledávání: '"BIGGIO, BATTISTA"'
Autor:
Ledda, Emanuele, Scodeller, Giovanni, Angioni, Daniele, Piras, Giorgio, Cinà, Antonio Emanuele, Fumera, Giorgio, Biggio, Battista, Roli, Fabio
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 its wide use, assumes high relevance for security-sensitive application
Externí odkaz:
http://arxiv.org/abs/2410.21952
Autor:
Piras, Giorgio, Pintor, Maura, Demontis, Ambra, Biggio, Battista, Giacinto, Giorgio, Roli, Fabio
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 inducing a misclassification. These methods, which we refer to as adversari
Externí odkaz:
http://arxiv.org/abs/2409.01249
Autor:
Villani, Francesco, Lazzaro, Dario, Cinà, Antonio Emanuele, Dell'Amico, Matteo, Biggio, Battista, Roli, Fabio
Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase. These attacks typically require re-clustering the entire dataset mult
Externí odkaz:
http://arxiv.org/abs/2408.07558
Autor:
Mura, Raffaele, Floris, Giuseppe, Scionis, Luca, Piras, Giorgio, Pintor, Maura, Demontis, Ambra, Giacinto, Giorgio, Biggio, Battista, Roli, Fabio
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 loss functions, optimizers, step-size schedulers, and default hyperpara
Externí odkaz:
http://arxiv.org/abs/2407.08806
Autor:
Scano, Christian, Floris, Giuseppe, Montaruli, Biagio, Demetrio, Luca, Valenza, Andrea, Compagna, Luca, Ariu, Davide, Piras, Luca, Balzarotti, Davide, Biggio, Battista
ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects malicious requests by matching them against the Core Rule Set (CRS), identifying well-known attack patterns. E
Externí odkaz:
http://arxiv.org/abs/2406.13547
Autor:
Chen, Zhang, Demetrio, Luca, Gupta, Srishti, Feng, Xiaoyi, Xia, Zhaoqiang, Cinà, Antonio Emanuele, Pintor, Maura, Oneto, Luca, Demontis, Ambra, Biggio, Battista, Roli, Fabio
Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is considered one of the main suspects of the neural networks' vulnerability t
Externí odkaz:
http://arxiv.org/abs/2406.10090
Autor:
Ponte, Andrea, Trizna, Dmitrijs, Demetrio, Luca, Biggio, Battista, Ogbu, Ivan Tesfai, Roli, Fabio
As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia -- which pursues an optimal performances in terms of detection rate and low false ala
Externí odkaz:
http://arxiv.org/abs/2405.14478
Deep learning-based malware detection systems are vulnerable to adversarial EXEmples - carefully-crafted malicious programs that evade detection with minimal perturbation. As such, the community is dedicating effort to develop mechanisms to defend ag
Externí odkaz:
http://arxiv.org/abs/2405.00392
Autor:
Cinà, Antonio Emanuele, Rony, Jérôme, Pintor, Maura, Demetrio, Luca, Demontis, Ambra, Biggio, Battista, Ayed, Ismail Ben, Roli, Fabio
Adversarial examples are typically optimized with gradient-based attacks. While novel attacks are continuously proposed, each is shown to outperform its predecessors using different experimental setups, hyperparameter settings, and number of forward
Externí odkaz:
http://arxiv.org/abs/2404.19460
The living-off-the-land (LOTL) offensive methodologies rely on the perpetration of malicious actions through chains of commands executed by legitimate applications, identifiable exclusively by analysis of system logs. LOTL techniques are well hidden
Externí odkaz:
http://arxiv.org/abs/2402.18329