Network Resilience Under Epidemic Attacks: Deep Reinforcement Learning Network Topology Adaptations

In this work, we proposed a Deep reinforcement learning (DRL)-based NETwork Adaptations for network Resilience algorithm, namely DeepNETAR, which aims to generate robust network topologies against epidemic attacks. In DeepNETAR, a DRL agent aims to generate a robust network topology against epidemic attacks by removing vulnerable edges or adding the least vulnerable edges, given multiple objectives of system security and performance. Most existing network topology adaptation algorithms have used the size of the giant component (SGC) to ensure service availability based on network connectivity. However, in real communication networks, where packets may be dropped either from the presence of inside attackers or congestion on long routes, a larger SGC does not necessarily ensure higher service availability. In addition, for the DRL agent to learn fast and handle multiple, conflicting system objectives, we considered vulnerability-based selection of adaptable edge candidates, fractal-based solution search, and diverse reward functions aiming to achieve multi-objective optimization. Via extensive simulation experiments, we analyzed what DeepNETAR-based schemes using different objectives can achieve those two conflicting system objectives and comparing existing and baseline counterparts.