Bridging the Closed-World Gap: Interpretable Anomaly Detection Via a Hybrid TSK-FCM Ensemble on the BETH Dataset

A fundamental paradox plagues the deployment of Machine Learning in cybersecurity: supervised models generalize poorly to novel, Out-of-Distribution OoD attacks, whereas unsupervised models often suffer from poor separability, leading to unmanageable false-alarm rates. This challenge is exemplifi...

Quantum Autoencoders for Anomaly Detection in Cybersecurity

Anomaly detection in cybersecurity is a challenging task, where normal events far outnumber anomalous ones with new anomalies occurring frequently. Classical autoencoders have been used for anomaly detection, but struggles in data-limited settings which quantum counterparts can potentially...