# New Directions in Automated Traffic Analysis

arXiv: https://arxiv.org/abs/2008.02695 · HTML: https://mlsanity.com/paper/2008.02695/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 26 of 26)

- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation](https://mlsanity.com/paper/2403.05822/) (2024)
- [Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency](https://mlsanity.com/paper/2205.09669/) (2022)
- [In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems](https://mlsanity.com/paper/2306.08853/) (2023)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection](https://mlsanity.com/paper/2503.03022/) (2025)
- [FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets](https://mlsanity.com/paper/2504.02174/) (2025)
- [Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic](https://mlsanity.com/paper/2511.20497/) (2025)
- [Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network](https://mlsanity.com/paper/2210.16719/) (2022)
- [AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification](https://mlsanity.com/paper/2308.02182/) (2023)
- [Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems](https://mlsanity.com/paper/2505.08816/) (2025)
- [Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane](https://mlsanity.com/paper/2506.05779/) (2025)
- [Open-Source Framework for Encrypted Internet and Malicious Traffic Classification](https://mlsanity.com/paper/2206.10144/) (2022)
- [FlowWise: Stateful Fast-Slow Model Serving for Streaming Traffic Intelligence](https://mlsanity.com/paper/2402.03694/) (2024)
- [Generic Multi-modal Representation Learning for Network Traffic Analysis](https://mlsanity.com/paper/2405.02649/) (2024)
- [Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO](https://mlsanity.com/paper/2406.01852/) (2024)
- [UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security](https://mlsanity.com/paper/2503.04174/) (2025)
- [PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation](https://mlsanity.com/paper/2602.08331/) (2026)
- [Towards Intelligent Network Management: Leveraging AI for Network Service Detection](https://mlsanity.com/paper/2310.09609/) (2023)
- [Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey](https://mlsanity.com/paper/2403.16149/) (2024)
- [Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams](https://mlsanity.com/paper/2503.05675/) (2025)
- [Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection](https://mlsanity.com/paper/2504.03742/) (2025)
- [Taming Data Challenges in ML-based Security Tasks Using Generative AI](https://mlsanity.com/paper/2507.06092/) (2025)
- [Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion](https://mlsanity.com/paper/2507.20115/) (2025)
- [TraceCodec: A Compiler-Backed Neural Codec for Network Traffic Traces](https://mlsanity.com/paper/2605.29941/) (2026)
- [One-Prompt Censorship Evasion via Generative Diffusion Models](https://mlsanity.com/paper/2606.22717/) (2026)

## Abstract (arXiv)

Despite the use of machine learning for many network traffic analysis tasks in security, from application identification to intrusion detection, the aspects of the machine learning pipeline that ultimately determine the performance of the model -- feature selection and representation, model selection, and parameter tuning -- remain manual and painstaking. This paper presents a method to automate many aspects of traffic analysis, making it easier to apply machine learning techniques to a wider variety of traffic analysis tasks. We introduce nPrint, a tool that generates a unified packet representation that is amenable for representation learning and model training. We integrate nPrint with automated machine learning (AutoML), resulting in nPrintML, a public system that largely eliminates feature extraction and model tuning for a wide variety of traffic analysis tasks. We have evaluated nPrintML on eight separate traffic analysis tasks and released nPrint and nPrintML to enable future work to extend these methods.