70 citations · 105 across the 12 of their papers we have counts for
4 papers · 1 filter
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection
Jordan F. Masakuna, DJeff Kanda Nkashama, Arian Soltani +3
Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly un…
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…
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…
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features
Thibault Duhamel, Mariane Maynard, Froduald Kabanza
The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investi…