# PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation

arXiv: https://arxiv.org/abs/2602.08331 · HTML: https://mlsanity.com/paper/2602.08331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts](https://mlsanity.com/paper/2206.02770/) (2022)
- [Multimodal Contrastive Training for Visual Representation Learning](https://mlsanity.com/paper/2104.12836/) (2021)
- [Factorized Contrastive Learning: Going Beyond Multi-view Redundancy](https://mlsanity.com/paper/2306.05268/) (2023)
- [NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation](https://mlsanity.com/paper/2310.08543/) (2023)
- [New Directions in Automated Traffic Analysis](https://mlsanity.com/paper/2008.02695/) (2020)
- [netFound: Principled Design for Network Foundation Models](https://mlsanity.com/paper/2310.17025/) (2023)
- [Robust Smartphone App Identification Via Encrypted Network Traffic Analysis](https://mlsanity.com/paper/1704.06099/) (2017)
- [Network Traffic Anomaly Detection Using Recurrent Neural Networks](https://mlsanity.com/paper/1803.10769/) (2018)
- [Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic](https://mlsanity.com/paper/2511.20497/) (2025)
- [WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence Matching](https://mlsanity.com/paper/2508.03151/) (2025)

## Built on by (most cited first, 1 of 1)

- [TraceCodec: A Compiler-Backed Neural Codec for Network Traffic Traces](https://mlsanity.com/paper/2605.29941/) (2026)

## Abstract (arXiv)

Network traffic classification underpins network security and management but faces growing challenges from pervasive encryption and evolving protocols. Existing representations involve clear trade-offs: hand-crafted flow statistics are efficient but lossy, raw-bit encodings can be accurate but costly, and pre-trained embeddings support transfer but often flatten the protocol stack and entangle cross-layer signals. We observe substantial redundancy both across and within network layers, which existing paradigms do not explicitly address, wasting capacity and encouraging shortcut learning that degrades generalization. To address this, we propose PACC, a redundancy-aware cross-layer representation framework. PACC models protocol layers as multiview inputs and learns faithful, compact projections factorized into shared (cross-layer) and private (layer-specific) components. It incorporates an information-preserving sparse segment representation to reduce structural repetition and supervised intra-layer filtering to suppress task-irrelevant semantic redundancy. A behavior-guided shared/private decomposition aligns cross-layer consensus while retaining layer-specific information, supported by information-theoretic analysis. Across encrypted application classification, IoT device identification, benign/malicious traffic classification, and Tor traffic detection, PACC achieves the best overall performance. On encrypted traffic, it achieves an F1 score of 0.9685, substantially outperforming existing baselines while using approximately 15x fewer computes and less GPU memory than the latest pre-trained traffic model.