A Lightweight and Optimal Defense System for DDoS Attacks in IoMT Networks

Integrating the Internet of Things (IoT) into the healthcare sector through the Internet of Medical Things (IoMT) has significantly enhanced patient care and the functionality of medical devices. However, this integration has introduced new challenges in cybersecurity, especially in detecting Distributed Denial of Service (DDoS) attacks. While various Machine Learning (ML)-based methods have been proposed to detect DDoS attacks, they face difficulty detecting both high-and low-volume DDoS attacks simultaneously. Additionally, there is a need to identify the optimal defense strategy to safeguard IoMT networks. This paper introduces a Lightweight And Optimal Defense System (LAMDA) for IoMT networks using a novel and efficient feature selection method called Threshold Feature Selection (TFS) with tree-based ML models. The system incorporates a game theory approach to identify the most effective defense strategies, enabling rapid and accurate decision-making during cyberattacks. The performance of the LAMDA system is evaluated using various datasets containing both high-and low-volume DDoS attacks. Results indicate that the LAMDA system, mainly when using the Random Forest model, achieves an accuracy rate of over 93% in detecting such attacks.