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Jean-Charles Verdier
Jean-Charles Verdier
Assistant research University of Sherbrooke
Verified email at usherbrooke.ca
Title
Cited by
Cited by
Year
A revealing large-scale evaluation of unsupervised anomaly detection algorithms
M Alvarez, JC Verdier, DJK Nkashama, M Frappier, PM Tardif, F Kabanza
arXiv preprint arXiv:2204.09825, 2022
162022
Robustness evaluation of deep unsupervised learning algorithms for intrusion detection systems
D Nkashama, A Soltani, JC Verdier, M Frappier, PM Tardif, F Kabanza
arXiv preprint arXiv:2207.03576, 2022
122022
The drawback of binary labeling for the evaluation of unsupervised intrusion detection algorithms
JC Verdier, DJK Nkashama, M Frappier, PM Tardif, F Kabanza
Marc and Tardif, Pierre-Martin and Kabanza, Froduald, The Drawback of Binary …, 0
2
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
DJK Nkashama, JM Félicien, A Soltani, JC Verdier, PM Tardif, M Frappier, ...
arXiv preprint arXiv:2407.08838, 2024
2024
Évaluation des algorithmes de détection d'anomalies non supervisés
JC Verdier
Université de Sherbrooke, 2023
2023
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems
KN D’Jeff, A Soltani, JC Verdier, M Frappier, PM Tardif, F Kabanza
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