Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models
arXiv:2605.26307v1 Announce Type: cross Abstract: Software-Defined Networking (SDN) provides flexible and programmable network management; however, its centralized control architecture remains highly vulnerable to Distributed Denial-of-Service (DDoS) attacks, particularly Carpet-Bombing DDoS attacks that distribute malicious traffic across multiple targets to evade conventional detection mechanisms. In this paper, a Retrieval-Augmented Generation (RAG)-based framework is proposed for real-time detection and mitigation of Carpet-Bombing DDoS attacks in SDN environments. The proposed framework c
The increasing complexity and scale of cyber threats necessitate advanced defense mechanisms, making the application of AI and RAG to network security a timely development.
This development highlights the growing integration of advanced AI models into foundational cybersecurity infrastructure, offering more sophisticated and adaptive defense against evolving threats.
Network defense against sophisticated DDoS attacks will increasingly rely on real-time, AI-driven analysis and mitigation strategies, moving beyond traditional signature-based detection.
- · Cybersecurity providers
- · Cloud infrastructure providers
- · AI/ML developers
- · Organizations with legacy security systems
- · Attackers relying on conventional DDoS methods
Enhanced resilience of Software-Defined Networking (SDN) environments against critical cyber threats.
Increased demand for AI-powered security solutions, potentially leading to new industry standards and regulatory requirements.
A potential 'AI arms race' in cybersecurity, with both attackers and defenders leveraging more sophisticated AI capabilities.
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Read at arXiv cs.AI