AI-Driven Intrusion Detection Systems for Securing IoT Healthcare Networks

Abstract

The integration of IoT in healthcare has remained very dynamic, with a lot of improvement in the health of patients and the running of operations. Integration also comes with new risks and threats, raising IoT healthcare networks as cyber victims with great potential. This study explores an AI-based solution to defend healthcare IoT networks against intrusions. Therefore, using the most superior machine learning algorithms and deep learning expertise, it is concluded that a credible IDS would be built eventually to be able to detect and neutralize security threats in a live environment. The proposed IDS are trained and tested on a large, rich data set of IoT healthcare security incidents and features like CNN and RNN. Our system has learned to identify numerous and different types of cyber threats, such as Malware, Ransomware, Unauthorized access, data breaches, and many more, with better accuracy and even fewer false positives. This study proves that IDS backed by Artificial Intelligence is effective in improving the security status of IoT healthcare networks, organization’s control over crucial patient information, and thus, the maintenance of the continuous provision of healthcare services.