Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks

Abstract

The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT-powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by using queuing theory concept to evaluate the network safety. Also, the network scenario will change due to the dynamic nature of flying vehicles. Simulation describes the queue length when the ground station is under attack. The proposed IDS utilizes the optimal threshold to make a tradeoff between false positive and false negative states with Markov binomial and Markov chain distribution stochastic models. However, at each time slot, the results demonstrate maintaining queue length in normal mode with less packet loss and high attack detection.