Predicting the Impact of Distributed Denial of Service (DDoS) Attacks in Long-Term Evolution for Machine (LTE-M) Networks Using a Continuous-Time Markov Chain (CTMC) Model

The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gather data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such attacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we developed a predictive model called Markov Detection and Prediction (MDP) using a Continuous-Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing, and predicting DDoS attacks in Long-Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious, and Malicious traffic. Additionally, we are able to predict the system behavior when facing different DDoS attacks.