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AI Research
arXiv CS.AI·May 27·8 min read
Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models
TL;DR
Carpet-bombing DDoS attacks (distributed denial-of-service floods targeting many services simultaneously) are difficult to detect in Software-Defined Networks (SDN), where network control is centralized in software. Researchers built a detection and mitigation system combining Retrieval-Augmented Generation (RAG)—which retrieves relevant knowledge before generating responses—with Large Language Models (LLMs) to intelligently identify and stop these attacks.
✦ Why It Matters
Engineers can deploy LLM-based detection to reduce DDoS response time and improve accuracy in SDN environments without constant manual tuning.
Key Takeaways
- Adopt RAG frameworks for improved DDoS attack detection.
- Utilize LLMs for contextual traffic analysis in SDN.
- Experiment with different traffic representation strategies.
- Focus on real-time mitigation to maintain network stability.
How It Works
The proposed framework combines traffic feature representation with semantic embedding generation to classify network behavior. It uses FAISS (Facebook AI Similarity Search) for efficient similarity retrieval and employs LLMs for contextual inference, allowing for rapid detection without traditional model training.