Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Poll, Erik"'
This paper explores the use of active and passive learning, i.e.\ active and passive techniques to infer state machine models of systems, for fuzzing. Fuzzing has become a very popular and successful technique to improve the robustness of software ov
Externí odkaz:
http://arxiv.org/abs/2406.08077
Publikováno v:
Proceedings of the 9th International Conference on Information Systems Security and Privacy 2023
Fuzz testing (or fuzzing) is an effective technique used to find security vulnerabilities. It consists of feeding a software under test with malformed inputs, waiting for a weird system behaviour (often a crash of the system). Over the years, differe
Externí odkaz:
http://arxiv.org/abs/2301.05060
Fuzzing is a security testing methodology effective in finding bugs. In a nutshell, a fuzzer sends multiple slightly malformed messages to the software under test, hoping for crashes or weird system behaviour. The methodology is relatively simple, al
Externí odkaz:
http://arxiv.org/abs/2301.02490
Autor:
Van Aubel, Pol, Poll, Erik
The field of electric vehicle charging involves a complex combination of actors, devices, networks, and protocols. These protocols are being developed without a clear focus on security. In this paper, we give an overview of the main roles and protoco
Externí odkaz:
http://arxiv.org/abs/2202.04631
While many defences against adversarial examples have been proposed, finding robust machine learning models is still an open problem. The most compelling defence to date is adversarial training and consists of complementing the training data set with
Externí odkaz:
http://arxiv.org/abs/2105.12427
Sensitivity to adversarial noise hinders deployment of machine learning algorithms in security-critical applications. Although many adversarial defenses have been proposed, robustness to adversarial noise remains an open problem. The most compelling
Externí odkaz:
http://arxiv.org/abs/2008.05247
Deep neural networks are at the forefront of machine learning research. However, despite achieving impressive performance on complex tasks, they can be very sensitive: Small perturbations of inputs can be sufficient to induce incorrect behavior. Such
Externí odkaz:
http://arxiv.org/abs/2008.04094
The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off
Externí odkaz:
http://arxiv.org/abs/2008.03046
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.