A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

The increasing growth of the Internet of Things (IoT) and its open and shared character has exponentially led to a rise in new attacks. Consequently, quick and adaptive detection of attacks in IoT environments is essential. The Intrusion Detection System (IDS) is responsible for protecting and detecting the type of attacks. Creating an IDS that works in real time and adapts to environmental changes is critical. In this paper, we propose a Deep Reinforcement Learning-based (DRL) self-learning IDS that addresses the mentioned challenges. DRL-based IDS helps to create a decision agent, who controls the interaction with the indeterminate environment and performs binary detection (normal/intrusion) in fog. We use the ensemble method to classify multi-class attacks in the cloud. The proposed approach was evaluated on the CIC-IDS2018 dataset. The results demonstrated that the proposed model achieves a superior performance in detecting intrusions and identifying attacks to compare other machine learning techniques and state-of-the-art approaches. For example, our suggested method can detect Botnet attacks with an accuracy of 0.9999% and reach an F-measure of 0.9959 in binary detection. It can reduce the prediction time to 0.52 also. Overall, we proved that combining multiple methods can be a great way for IDS.