An Efficient Impersonation Attack DetectionMethod 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).