Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks

In this paper, a new reinforcement learning intrusion detection system is developed for IoT networks incorporated with WSNs. A research is carried out and the proposed model RL-IDS plot is shown, where the detection rate is improved. The outcome shows a decrease in false alarm rates and is compared with the current methodologies. Computational analysis is performed, and then the results are compared with the current methodologies, i.e. distributed denial of service (DDoS) attack. The performance of the network is estimated based on security and other metrics. Keywords—DDoS, intrusion detection, IoT, machine learning, Markov decision process (MDP), Q-learning, NSL-KDD, reinforcement-learning.