Improved Chaotic Firefly with Light Gradient Boosting Machine Based Intrusion Detection System for Smart Healthcare
Ramesh Tanty, Pandit Byomakesha Dash, Janmenjoy Nayak, Bighnaraj Naik · 2024
The Internet of Medical Things (IoMT) is an emerging concept within the expanding domain of the Internet of Things (IoT) designed to enhance healthcare operations and facilitate remote patient monitoring. Nonetheless, these devices are vulnerable to cyber-attacks posing risks to healthcare operations and patient safety. To detect and minimize attacks on the 10MT, approaches like as Intrusion Detection Systems (IDS) and threat intelligence are recommended. As attackers refine their approaches, there is a developing tendency towards using intelligent methodologies for improved and active attack detection. This research presents a Light Gradient Boosting Machine with Chaotic Firefly Algorithm (LGBM _ CFA) IDS designed specifically for the IoMT. The hyper-parameters of the LGBM model have been fine-tuned by implementing the CFA approach to address the problems of underfitting and overfitting. The proposed model's performance has been thoroughly evaluated by comparing it to current IDS IoMT models, such as LGBM with Particle Swarm Optimization (PSO), Firefly Algorithm (FA) techniques, Tent Map FA (TFA) and Hennon Map FA (HFA). The findings demonstrate that the suggested model attains a remarkable accuracy of 99.77%, which indicates its efficacy in accurately detecting intrusions. The findings of this study contribute to the advancement of IDS models that are more precise and effective in IoMT environments.