Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Demetrio, Luca"'
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
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
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
Autor:
Angioni, Daniele, Demetrio, Luca, Pintor, Maura, Oneto, Luca, Anguita, Davide, Biggio, Battista, Roli, Fabio
Machine-learning models demand for periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly-updated model may commit mistakes that the previous model did not make. Such misclassification
Externí odkaz:
http://arxiv.org/abs/2402.17390
Autor:
Montaruli, Biagio, Demetrio, Luca, Pintor, Maura, Compagna, Luca, Balzarotti, Davide, Biggio, Battista
Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage. Nevertheless, the attacks recently proposed have demonstrated limited effectiveness due to their lack
Externí odkaz:
http://arxiv.org/abs/2310.03166