Genetic Algorithms-based Feature Selection (GAFS-IDS) for Attack Detection in the Internet of Medical Things Network
Revoori Swetha, Sudha S. Senthilkumar · 2024
The computer environment has been ideally changed due to the improvements in information and communication technology (ICT). These improvements resulted in a novel path for communication in the Internet of Things (IoT). Later, the Internet of Things has become the most innovative techniques for creating intelligent ideas. The IoMT (Internet of Medical Things) devices are mainly used for data sharing and communicating among those devices. Because of these advancements in the medical field, the healthcare industry provides greater attention to patient’s data to avoid data theft. Security measures should be taken on any technology that depends on the Internet of Things. The primary goal of this study is to show how the IDS is effective in detecting attacks in the IoMT environment by using machine learning models. The research is about detecting the attacks by selecting the features from the dataset using a genetic algorithm to categorize the attacks. The suggested GA-Bagging technique yields a $\mathbf{9 6 \%}$ accuracy rate on IoMT data.