September 2026

Encrypted traffic analysis (ETA) plays a critical role in detecting malicious activities. Although machine learning-based ETA has shown significant promise, existing methods heavily depend on large-sc

The human hand serves as a vital biometric modality, with the palm, fingers, and knuckles commonly employed for identity recognition across a range of applications. Despite the abundance of sensitive

The rapid advancement of generative adversarial networks and diffusion-based models has enabled the creation of highly realistic synthetic images, raising urgent concerns regarding the authenticity of

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