Man-in-the-Middle attack Explainer for Fog computing using Soft Actor Critic Q-Learning Approach

Because of exponential growth in the availability of large number of Internet of Things (IoT) devices there is an increase in the latency of IoT applications that is managed by performing computation on edge devices/fog nodes. Man-in-the-Middle (MitM) attack is very much common in fog computing as the Fog computing architecture is vulnerable to MitM attack because of the positioning of fog nodes between cloud and end devices. Several machine learning approaches are designed and developed in literature for detection of MitM attacks in fog computing but they lack interpretability/explainability feature. In this paper a novel interpretable Q-learning algorithm with soft actor critic approach is designed for detecting MitM attacks in Fog computing with proper reasoning. Entropy regularized reinforcement learning is performed at each time step which prevents the loss during of every Q-function during approximation of the target. The attack detection policies formulated are of high quality as it satisfies the quality assurance metrics of robustness and correctness the conduct of the proposed interpretable Q-learning framework is encouraging towards the metrics like latency, attack detection time, energy consumption, and accuracy.