Deep Learning for Radio Resource Allocation Under DoS Attack

In this paper, we focus on the problem of remote state estimation in wireless networked cyber-physical systems (CPS). Information from multiple sensors is transmitted to a central gateway over a wireless network with fewer channels than sensors. Channel and power allocation are performed jointly, in the presence of a denial of service (DoS) attack where one or more channels are jammed by an attacker transmitting spurious signals. The attack policy is unknown and the central gateway has the objective of minimizing state estimation error with maximum energy efficiency. The problem involves a combination of discrete and continuous action spaces. In addition, the state and action spaces have high dimensionality, and the channel states are not fully known to the defender. We propose an innovative model-free deep reinforcement learning (DRL) algorithm to address the problem. In addition, we develop a deep learning-based method with a novel deep neural network (DNN) structure for detecting changes in the attack policy post-training. The proposed online policy change detector accelerates the adaptation of the defender to a new attack policy and also saves computational resources compared to continuous training. In short, a complete system featuring a DRL-based defender that is trained initially and adapts continually to changes in attack policy has been developed. Our numerical results show that the proposed intelligent system can significantly enhance the resilience of the system to DoS attacks.