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Charpentier, A., Boulahia Cuppens, N., Cuppens, F., & Yaich, R. (août 2022). Deep reinforcement learning-based defense strategy selection [Communication écrite]. 17th International Conference on Availability, Reliability and Security (ARES 2022), Vienna, Austria (11 pages). Lien externe
Crochelet, P., Neal, C., Boulahia Cuppens, N., & Cuppens, F. (décembre 2022). Attacker Attribution via Characteristics Inference Using Honeypot Data [Communication écrite]. 16th International Conference on Network and System Security (NSS 2022), Denarau Island, Fiji. Lien externe
Katsikas, S. K., Lambrinoudakis, C., Boulahia Cuppens, N., Mylopoulos, J., Kalloniatis, C., Meng, W., Furnell, S., Pallas, F., Pohle, J., Sasse, M. A., Abie, H., Ranise, S., Verderame, L., Cambiaso, E., Vidal, J. M., & Sotelo Monge, M. A. (octobre 2022). Computer security [Communication écrite]. International Workshops on Computer Security (CyberICPS, SECPRE, ADIoT, SPOSE, CPS4CIP and CDT&SECOMANE), Darmstadt, Germany. Lien externe
Merzouk, M. A., Cuppens, F., Boulahia Cuppens, N., & Yaich, R. (2022). Investigating the practicality of adversarial evasion attacks on network intrusion detection. Lien externe
Merzouk, M. A., Delas, J., Neal, C., Cuppens, F., Boulahia Cuppens, N., & Yaich, R. (août 2022). Evading deep reinforcement learning-based network intrusion detection with adversarial attacks [Communication écrite]. 17th International Conference on Availability, Reliability and Security (ARES 2022), Vienna, Austria (6 pages). Lien externe
Moussaileb, R., Boulahia Cuppens, N., Lanet, J.-L., & Bouder, H. L. (2022). A Survey on Windows-based Ransomware Taxonomy and Detection Mechanisms: Case Closed? ACM Computing Surveys, 54(6), 1-36. Lien externe
