Zobrazeno 1 - 10
of 1 165
pro vyhledávání: '"TROÌA, A."'
Publikováno v:
Expert Systems with Applications, Volume 125, 1 July 2019, Pages 130-141
In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and firewalls typically will not have access to encryption keys, and therefo
Externí odkaz:
http://arxiv.org/abs/2312.04596
Autor:
Chiara Agnoli, Michele Tumbarello, Kateryna Vasylyeva, Carola S. Selva Coddè, Erika Monari, Marta Gruarin, Roberta Troìa, Francesco Dondi
Publikováno v:
Journal of Veterinary Internal Medicine, Vol 38, Iss 5, Pp 2480-2494 (2024)
Abstract Background Benefit of adding a second‐line immunosuppressive drug to glucocorticoids for the treatment of non‐associative immune‐mediated hemolytic anemia (naIMHA) in dogs has not been defined prospectively. Hypothesis/Objectives Evalu
Externí odkaz:
https://doaj.org/article/85b1ec93334c43d5acfbd8151f4873ab
Autor:
Raghuvir, Yuvaraj Athur, Govindarajan, Senthil, Vijayakumar, Sanjeevi, Yadlapalli, Pradeep, Di Troia, Fabio
Publikováno v:
Proceedings of the Future Technologies Conference (FTC) 2021, Volume 3. Springer International Publishing, 2022
Secure computation protocols combine inputs from involved parties to generate an output while keeping their inputs private. Private Set Intersection (PSI) is a secure computation protocol that allows two parties, who each hold a set of items, to lear
Externí odkaz:
http://arxiv.org/abs/2308.14741
Effective and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One
Externí odkaz:
http://arxiv.org/abs/2307.10256
Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results. As a result, antivirus developers are incorporating machine learning models into their products. While these models improve malware d
Externí odkaz:
http://arxiv.org/abs/2306.13587
A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose, investigating how existing malware families are developed and examinin
Externí odkaz:
http://arxiv.org/abs/2305.00605
Autor:
Müller, Jasper, Slyne, Frank, Kaeval, Kaida, Troia, Sebastian, Fehenberger, Tobias, Elbers, Jörg-Peter, Kilper, Daniel C., Ruffini, Marco, Mas-Machuca, Carmen
SNR margins between partially and fully loaded DWDM systems are estimated without detailed knowledge of the network. The ML model, trained on simulation data, achieves accurate predictions on experimental data with an RMSE of 0.16 dB.
Comment: T
Comment: T
Externí odkaz:
http://arxiv.org/abs/2302.08275
Autor:
Kaeval, K., Slyne, F., Troia, S., Kenny, E., Große, K., Griesser, H., Kilper, D. C., Ruffini, M., Pedreno-Manresa, J-J, Patri, S. K., Jervan, G.
Optical Spectrum as a Service (OSaaS) spanning over multiple transparent optical network domains, can significantly reduce the investment and operational costs of the end-to-end service. Based on the black-link approach, these services are empowered
Externí odkaz:
http://arxiv.org/abs/2302.04623
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger resul
Externí odkaz:
http://arxiv.org/abs/2207.00620
For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract features from malware executable files and represent them as images using var
Externí odkaz:
http://arxiv.org/abs/2207.00421