7 citations · 14 across the 3 of their papers we have counts for
3 papers
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
D'Jeff K. Nkashama, Jordan Masakuna Félicien, Arian Soltani +4
Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learni…
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems
D'Jeff Kanda Nkashama, Arian Soltani, Jean-Charles Verdier +3
Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demon…
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms
Maxime Alvarez, Jean-Charles Verdier, D'Jeff K. Nkashama +3
Anomaly detection has many applications ranging from bank-fraud detection and cyber-threat detection to equipment maintenance and health monitoring. However, choosing a suitable al…