| 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 | 20 | 2022 |
| 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 | 19 | 2022 |
| 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 | | |
