Coordinated Multi-Agent Moving Target Defense Against Cross-Slice Lateral Movement in Virtualized SDN Clouds

Cross-slice lateral movement attacks pose a critical security threat to NFV/SDN-based 6G networks, where adversaries exploit compromised Virtual Network Functions (VNFs) to infiltrate other slices. While prior works have employed Deep QLearning (DQL) for Moving Target Defense (MTD), these single-agent solutions lack coordination and scalability across network slices. To overcome these limitations, this paper proposes a MultiAgent Deep Deterministic Policy Gradient (MADDPG) framework for orchestrating Software-Defined Networking (SDN)-based path randomization in service-oriented cloud networks based on NFV and SDN principles. The architecture leverages centralized training with decentralized execution, enabling collaborative decision-making among agents assigned to individual slices. Evaluated in a realistic OpenStack testbed with Tacker integration, the proposed method achieves over 60% reduction in attack success rate and a 2.5× improvement in Mean Time-To-Compromise (MTTC) compared to random path-hopping, all while maintaining low latency (<50ms) and minimal control overhead (<20 flow modifications per minute). These results validate the effectiveness and deployability of the proposed multi-agent MTD approach in real-world 6G cloud infrastructures.