Zobrazeno 1 - 10
of 1 227
pro vyhledávání: '"Colajanni, A."'
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
Publikováno v:
Array, Vol 24, Iss , Pp 100365- (2024)
Modern cybersecurity best practices and standards require continuous Vulnerability Assessment (VA) and Penetration Test (PT). These activities are human- and time-expensive. The research community is trying to propose autonomous or semi-autonomous so
Externí odkaz:
https://doaj.org/article/eb1f9eae75b2488c91ce56d34e40bee4
Phishing kits are tools that dark side experts provide to the community of criminal phishers to facilitate the construction of malicious Web sites. As these kits evolve in sophistication, providers of Web-based services need to keep pace with continu
Externí odkaz:
http://arxiv.org/abs/2210.08273
Publikováno v:
In Array December 2024 24
Publikováno v:
In Structures January 2025 71
This paper proposes a novel approach for the study of cyber-attacks against the powertrain of a generic vehicle. The proposed model is composed by a a generic Internal Combustion engine and a speed controller, that communicate through a Controller Ar
Externí odkaz:
http://arxiv.org/abs/2202.00743
Publikováno v:
Buildings, Vol 14, Iss 10, p 3256 (2024)
Braces equipped with dissipative devices are among the most widespread methods for the seismic strengthening of seismically prone reinforced concrete (RC) frames. It allows for high reductions in seismic vulnerability with inexpensive, quickly execut
Externí odkaz:
https://doaj.org/article/e5f5cef4b8044c26ab07b5b0a89bf9c7
Publikováno v:
In Ad Hoc Networks 1 April 2024 156
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial att
Externí odkaz:
http://arxiv.org/abs/2106.09380
Publikováno v:
In Procedia Structural Integrity 2024 64:277-284