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pro vyhledávání: '"Dambra, Savino"'
Autor:
Dambra, Savino, Han, Yufei, Aonzo, Simone, Kotzias, Platon, Vitale, Antonino, Caballero, Juan, Balzarotti, Davide, Bilge, Leyla
Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis techniques for
Externí odkaz:
http://arxiv.org/abs/2307.14657
In the past decade, global warming made several headlines and turned the attention of the whole world to it. Carbon footprint is the main factor that drives greenhouse emissions up and results in the temperature increase of the planet with dire conse
Externí odkaz:
http://arxiv.org/abs/2304.00927
Previous work has investigated the particularities of security practices within specific user communities defined based on country of origin, age, prior tech abuse, and economic status. Their results highlight that current security solutions that ado
Externí odkaz:
http://arxiv.org/abs/2301.07346
Autor:
Apruzzese, Giovanni, Anderson, Hyrum S., Dambra, Savino, Freeman, David, Pierazzi, Fabio, Roundy, Kevin A.
Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can w
Externí odkaz:
http://arxiv.org/abs/2212.14315
Publikováno v:
ACM Transactions on Privacy and Security
ACM Transactions on Privacy and Security, 2023, 26 (2), pp.1-30. ⟨10.1145/3565362⟩
ACM Transactions on Privacy and Security, 2023, 26 (2), pp.1-30. ⟨10.1145/3565362⟩
Malware is still a widespread problem, and it is used by malicious actors to routinely compromise the security of computer systems. Consumers typically rely on a single AV product to detect and block possible malware infections, while corporations of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55247ad679021681f65c35ce1d479ef3
https://doi.org/10.1145/3565362
https://doi.org/10.1145/3565362
Publikováno v:
S&P 2020, 41st IEEE Symposium on Security and Privacy, 18-20 May 2020, San Francisco, CA, USA
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1093::663ec219be0619cdbe5b01c63c36958d
http://www.eurecom.fr/publication/6064
http://www.eurecom.fr/publication/6064