September 2026

Distributed learning frameworks, which partition neural network models across multiple computing nodes, enhance efficiency in collaborative edge-cloud systems, but may also introduce new vulnerabiliti

Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high com

Hybrid kernel fuzzing combines kernel fuzzing and symbolic execution: symbolic execution resolves hard-to-reach branch conditions, while kernel fuzzing mitigates path explosion. This collaboration has

Accurate IoT device identification is critical to network forensics, access control, and anomaly detection in large-scale, security-sensitive environments. Federated learning (FL) provides a decentral

Multivariate Time Series (MTS) anomaly detection is critical for various applications. Yet, most existing methods rely on forecasting or reconstruction-based frameworks, which suffer from inherent unc

Urban-scale video analytics systems hold significant promise for traffic monitoring, pedestrian flow estimation and public safety. However, the acquisition and processing of spatio-temporal data extra

Accurate network traffic classification is critical for defending against evolving cyber threats. However, mainstream methods relying solely on intra-flow features fail to distinguish malicious traffi

Threshold attribute-based anonymous credentials improve the security of traditional credentials systems by distributing the power of credential issuance across multiple independent issuers, and have a

Intelligent spoofing poses a critical threat to unmanned aerial vehicle (UAV) communications by corrupting synchronization and channel estimation while evading power-based detection. This paper presen

Heterogeneous person re-identification (ReID) aims to match different captured images of the same individual in challenging situations where the images exhibit strong differences, for instance, visibl