Proactive defense for cloud-native network slicing: A risk-aware intelligent moving target defense framework based on multi-agent deep reinforcement learning

This research paper describes a defensive framework for cloud-native network slicing (dividing a network into isolated virtual segments) that uses multi-agent deep reinforcement learning (a type of AI where multiple learning agents work together to make decisions) to protect against security threats. The framework takes a proactive approach by continuously changing the network configuration to make it harder for attackers to find vulnerabilities, similar to a moving target that's difficult to hit.