2 papers
stat.ML2025
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies
Matthew Lau, Tian-Yi Zhou, Xiangchi Yuan +3
Anomaly detection (AD) is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to…
stat.ML2024
Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights
Tian-Yi Zhou, Matthew Lau, Jizhou Chen +2
In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previously observed behaviors. Motivated by th…