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A word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Charpentier, A., Neal, C., Boulahia Cuppens, N., Cuppens, F., & Yaich, R. (2023, August). Real-Time Defensive Strategy Selection via Deep Reinforcement Learning [Paper]. 18th International Conference on Availability, Reliability and Security (ARES 2023), Benevento, Italy (11 pages). External link
Charpentier, A., Boulahia Cuppens, N., Cuppens, F., & Yaich, R. (2022, August). Deep reinforcement learning-based defense strategy selection [Paper]. 17th International Conference on Availability, Reliability and Security (ARES 2022), Vienna, Austria (11 pages). External link
Faillon, M.-A., Bout, B., Francq, J., Neal, C., Boulahia Cuppens, N., Cuppens, F., & Yaich, R. (2024, September). How to Better Fit Reinforcement Learning for Pentesting: A New Hierarchical Approach [Paper]. 29th European Symposium on Research in Computer Security (ESORICS 2024), Bydgoszcz, Poland. External link
Merzouk, M. A., Beurier, E., Yaich, R., Boulahia Cuppens, N., Cuppens, F., & Khomh, F. (2025, June). Diffusion-Based Adversarial Purification for Intrusion Detection [Paper]. 39th IFIP WG 11.3 Annual Conference on Data and Applications Security and Privacy (DBSec 2025), Gjøvik, Norway. Published in Lecture notes in computer science. External link
Merzouk, M. A., Neal, C., Delas, J., Yaich, R., Boulahia-Cuppens, N., & Cuppens, F. (2024). Adversarial robustness of deep reinforcement learning-based intrusion detection. International Journal of Information Security, 27 pages. External link
Merzouk, M. A., Cuppens, F., Boulahia Cuppens, N., & Yaich, R. (2023, August). Parameterizing poisoning attacks in federated learning-based intrusion detection [Paper]. 18th International Conference on Availability, Reliability and Security (ARES 2023), Benevento, Italy (8 pages). External link
Merzouk, M. A., Delas, J., Neal, C., Cuppens, F., Boulahia Cuppens, N., & Yaich, R. (2022, August). Evading deep reinforcement learning-based network intrusion detection with adversarial attacks [Paper]. 17th International Conference on Availability, Reliability and Security (ARES 2022), Vienna, Austria (6 pages). External link
Merzouk, M. A., Cuppens, F., Boulahia Cuppens, N., & Yaich, R. (2022). Investigating the practicality of adversarial evasion attacks on network intrusion detection. External link
Merzouk, M. A., Cuppens, F., Boulahia Cuppens, N., & Yaich, R. (2020, November). A Deeper Analysis of Adversarial Examples in Intrusion Detection [Paper]. 15th International Conference on Risks and Security of Internet and Systems (CRISIS 2020), Paris, France. External link
Yaich, R., Boulahia Cuppens, N., & Cuppens, F. (2017, August). Enabling Trust Assessment in Clouds-of-Clouds: A Similarity-Based Approach [Paper]. 12th International Conference on Availability, Reliability and Security (ARES 2017), Reggio, Calabria, Italy. External link