Abstract
This work presents a Moving Target Defense (MTD) framework for the protection of network slices and virtual resources in a telco cloud environment. The preliminary implementation provides a closed-loop security management of services with proactive MTD operations to reduce the success probability of attacks, and reactive MTD operations, empowered by a tampering detection and a traffic-based anomaly detection system. MTD strategies are adaptive and optimized with deep reinforcement learning (deep-RL) for balancing costs, security, and availability goals defined in a Multi-Objective Markov Decision Process (MOMDP).
Soussi, Wissem