Quantum AI for Cybersecurity: A Hybrid Quantum-Classical Models for Attack Path Analysis

Modern cyberattacks are increasingly complex, posing significant challenges to classical machine learning methods, particularly when labeled data is limited and feature interactions are highly non-linear. In this study we investigates the potential of hybrid quantum-classical learning to enhance...

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-Sphere Learning

Insider threat detection aims to identify malicious user behavior by analyzing logs that record user interactions. Due to the lack of fine-grained behavior-level annotations, detecting specific behavior-level anomalies within user behavior sequences is challenging. Unsupervised methods face high...

Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning

Federated learning FL enhances privacy and reduces communication cost for resource-constrained edge clients by supporting distributed model training at the edge. However, the heterogeneous nature of such devices produces diverse, non-independent, and identically distributed non-IID data, making t...