arXiv AI
Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classification
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arXiv AI
Jun 4
Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis
arXiv:2606. 04517v1 Announce Type: cross Abstract: Graph-based deep learning methods have been widely employed in encrypted traffic analysis to exploit latent correlations across different granularities.
arXiv Machine Learning
Jul 9
Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism
arXiv:2506. 01260v2 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.
arXiv AI
Aug 26
Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classification
arXiv:2603.25507v2 Announce Type: replace-cross Abstract: Network Traffic Classification (NTC) increasingly relies on data-driven models, yet its practical deployment is often constrained by limited...
arXiv Machine Learning
Jun 16
FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding
arXiv:2606. 16359v1 Announce Type: cross Abstract: Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead.
arXiv Machine Learning
Aug 6
Learning Compression Rules for Network Traffic
arXiv:2608. 04545v1 Announce Type: new Abstract: We study the problem of learning compact rule-based compressors for structured network traffic.
arXiv Machine Learning
1d ago
OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design
arXiv:2609.16898v1 Announce Type: cross Abstract: Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with...