no code implementations • 7 Sep 2022 • Arne Gevaert, Jonathan Peck, Yvan Saeys
In this work, we present an algorithm to distill the policy from a deep Q-network into a compact neuro-fuzzy controller.
1 code implementation • 7 Jul 2020 • Utku Ozbulak, Jonathan Peck, Wesley De Neve, Bart Goossens, Yvan Saeys, Arnout Van Messem
Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks.
no code implementations • 12 Mar 2020 • Raaghavi Sivaguru, Jonathan Peck, Femi Olumofin, Anderson Nascimento, Martine De Cock
We found that the DGA classifiers that rely on both the domain name and side information have high performance and are more robust against adversaries.
no code implementations • 3 May 2019 • Jonathan Peck, Claire Nie, Raaghavi Sivaguru, Charles Grumer, Femi Olumofin, Bin Yu, Anderson Nascimento, Martine De Cock
In this work, we present a novel DGA called CharBot which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM. MI (a deep learning approach).
no code implementations • NeurIPS 2017 • Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys
The input-output mappings learned by state-of-the-art neural networks are significantly discontinuous.