Modern networks generate massive amounts of diverse traffic that must be continuously monitored for security and performance — but traditional network traffic analysis systems often fall short. Whether rule-based or powered by machine learning, they commonly suffer from high false positives and poor explainability, making it hard for analysts to trust their outputs.
💡 Meet ReGAIN — a multi-stage framework that combines:
- Traffic Summarization
- Retrieval-Augmented Generation (RAG)
- Large Language Model (LLM) Reasoning
The goal? Deliver accurate, transparent, and evidence-backed network traffic analysis.
✅ How ReGAIN Works
ReGAIN converts network traffic into natural-language summaries and stores them in a multi-collection vector database. It then uses a hierarchical retrieval pipeline to ground LLM outputs with real, verifiable evidence.
Key components include:
- 🔎 Metadata-based filtering
- 🎯 MMR sampling
- 🔁 Two-stage cross-encoder reranking
- 🛑 Abstention mechanism to prevent hallucinations
This ensures decisions are not only correct, but also explainable and trustworthy.
📊 Real-World Performance
Evaluated on ICMP ping flood and TCP SYN flood traces from real-world datasets, ReGAIN achieved:
95.95% – 98.82% accuracy across different attack types and benchmarks
Validation came from:
✔ Ground truth datasets
✔ Human expert assessments
Even better — ReGAIN outperformed:
- Rule-based systems
- Classical ML models
- Deep learning baselines
…while still providing human-readable explanations instead of black-box outputs.
🔐 Why This Matters
Security teams need tools that are:
- Reliable
- Interpretable
- Evidence-backed
ReGAIN bridges the gap between advanced AI capabilities and real-world trust requirements in cybersecurity operations.
📚 Read the full paper here:
https://arxiv.org/abs/2512.22223
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