Zobrazeno 1 - 10
of 173
pro vyhledávání: '"Mancini, Luigi V"'
Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex relat
Externí odkaz:
http://arxiv.org/abs/2409.18736
The unprecedented availability of training data fueled the rapid development of powerful neural networks in recent years. However, the need for such large amounts of data leads to potential threats such as poisoning attacks: adversarial manipulations
Externí odkaz:
http://arxiv.org/abs/2403.13523
Autor:
Hitaj, Dorjan, Pagnotta, Giulio, De Gaspari, Fabio, Ruko, Sediola, Hitaj, Briland, Mancini, Luigi V., Perez-Cruz, Fernando
Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in publ
Externí odkaz:
http://arxiv.org/abs/2403.03593
Autor:
Miho, Hristofor, Pagnotta, Giulio, Hitaj, Dorjan, De Gaspari, Fabio, Mancini, Luigi V., Koubouris, Georgios, Godino, Gianluca, Hakan, Mehmet, Diez, Concepcion Muñoz
The easy and accurate identification of varieties is fundamental in agriculture, especially in the olive sector, where more than 1200 olive varieties are currently known worldwide. Varietal misidentification leads to many potential problems for all t
Externí odkaz:
http://arxiv.org/abs/2303.00431
Autor:
Pagnotta, Giulio, De Gaspari, Fabio, Hitaj, Dorjan, Andreolini, Mauro, Colajanni, Michele, Mancini, Luigi V.
Publikováno v:
IEEE Transactions on Information Forensics and Security, 2023
Moving Target Defense and Cyber Deception emerged in recent years as two key proactive cyber defense approaches, contrasting with the static nature of the traditional reactive cyber defense. The key insight behind these approaches is to impose an asy
Externí odkaz:
http://arxiv.org/abs/2303.00387
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future. Consequently, significant effort has been devoted to ransomware detection and mitigation. Behavioral-based ransomwar
Externí odkaz:
http://arxiv.org/abs/2301.11050
Watermarking of deep neural networks (DNNs) has gained significant traction in recent years, with numerous (watermarking) strategies being proposed as mechanisms that can help verify the ownership of a DNN in scenarios where these models are obtained
Externí odkaz:
http://arxiv.org/abs/2202.06091
Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without having to reveal the actual private
Externí odkaz:
http://arxiv.org/abs/2201.08786
Autor:
Piskozub, Michal, De Gaspari, Fabio, Barr-Smith, Frederick, Mancini, Luigi V., Martinovic, Ivan
Economic incentives encourage malware authors to constantly develop new, increasingly complex malware to steal sensitive data or blackmail individuals and companies into paying large ransoms. In 2017, the worldwide economic impact of cyberattacks is
Externí odkaz:
http://arxiv.org/abs/2106.00541
Recent advances in generative machine learning models rekindled research interest in the area of password guessing. Data-driven password guessing approaches based on GANs, language models and deep latent variable models have shown impressive generali
Externí odkaz:
http://arxiv.org/abs/2105.06165