44 citations · 62 across the 8 of their papers we have counts for
8 papers
Evaluating the Fork-Awareness of Coverage-Guided Fuzzers
Marcello Maugeri, Cristian Daniele, Giampaolo Bella +1
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 s…
Fuzzers for stateful systems: Survey and Research Directions
Cristian Daniele, Seyed Behnam Andarzian, Erik Poll
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 crash…
Security of EV-Charging Protocols
Pol Van Aubel, Erik Poll
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 secu…
Deep Repulsive Prototypes for Adversarial Robustness
Alex Serban, Erik Poll, Joost Visser
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 adversa…
Adversarial Examples on Object Recognition: A Comprehensive Survey
Alex Serban, Erik Poll, Joost Visser
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 pertur…
Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise
Alex Serban, Erik Poll, Joost Visser
Sensitivity to adversarial noise hinders deployment of machine learning algorithms in security-critical applications. Although many adversarial defenses have been proposed, robustn…