arXiv AI
Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classification
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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 AI
1d ago
The Split Protocol Decides the Result: Re-Evaluating Encrypted Traffic Classification
The paper demonstrates that encrypted traffic classification accuracy can be dramatically inflated by the choice of data split protocol. Using the same model and code, macro‑F1 scores drop from 0.992 under a standard random split to 0.567 when flows from a single packet capture are kept on one side of the split, a gap of 0.426 that is statistically significant. The authors evaluate multiple split strategies on QUIC traffic, showing that server‑disjoint splitting harms performance while other splits are comparable, and provide a diagnostic based on single‑identifier probes to identify the appropriate split axis before training.
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
Oct 21
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections (Full Version)
LightSplit introduces a lightweight orthogonal random projection at the split learning cut layer to reduce communication overhead and limit information exposure. By sending low‑dimensional projections instead of raw activations, the method preserves end‑to‑end differentiability while discarding irreversibly part of the representation, thereby mitigating reconstruction attacks. Experiments on seven benchmarks show up to 32× compression with minimal accuracy loss and strong resistance to state‑of‑the‑art inversion attacks.
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.