An Efficient Impersonation Attack Detection Method in Fog Computing

Fog computing paradigm extends computing, communication, storage, and network resources to the network's edge. As the fog layer is located between cloud and end-users, it can provide more convenience and timely services to end-users. However, in fog computing (FC), attackers can behave as real fog nodes or end-users to provide malicious services in the network. The attacker acts as an impersonator to impersonate other legitimate users. Therefore, in this work, we present a detection technique to secure the FC environment. First, we model a physical layer key generation based on wireless channel characteristics. To generate the secret keys between the legitimate users and avoid impersonators, we then consider a Double Sarsa technique to identify the impersonators at the receiver end. We compare our proposed Double Sarsa technique with the other two methods to validate our work, i.e., Sarsa and Q-learning. The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate (FAR), miss detection rate (MDR), and average error rate (AER).