Zobrazeno 1 - 10
of 788
pro vyhledávání: '"Roli, Fabio"'
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
A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification
Autor:
Zhang, Lu, Lambotharan, Sangarapillai, Zheng, Gan, Liao, Guisheng, Demontis, Ambra, Roli, Fabio
Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless net
Externí odkaz:
http://arxiv.org/abs/2407.06807
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vulnerable to carefully crafted attacks called adversarial examples. Hence
Externí odkaz:
http://arxiv.org/abs/2407.06796
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
Distributed model fitting refers to the process of fitting a mathematical or statistical model to the data using distributed computing resources, such that computing tasks are divided among multiple interconnected computers or nodes, often organized
Externí odkaz:
http://arxiv.org/abs/2406.00703
Machine learning malware detectors are vulnerable to adversarial EXEmples, i.e. carefully-crafted Windows programs tailored to evade detection. Unlike other adversarial problems, attacks in this context must be functionality-preserving, a constraint
Externí odkaz:
http://arxiv.org/abs/2405.14519
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
Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples
Adversarial EXEmples are carefully-perturbed programs tailored to evade machine learning Windows malware detectors, with an on-going effort in developing robust models able to address detection effectiveness. However, even if robust models can preven
Externí odkaz:
http://arxiv.org/abs/2405.02646
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