Federated Reinforcement Learning-Supported IDS for IoT-steered Healthcare Systems
Safa Otoum, Nadra Guizani, Hussein T. Mouftah
Abstract
Wireless Networks lack clear boundaries which leads to security concerns and vulnerabilities to numerous kinds of intrusions. With the growth of cyber intruders, the risks on crucial applications monitored by networked systems have also grown. Effective and vigorous Intrusion Detection Systems (IDSs) for protecting shared information continues to be an essential task to keep private data safe especially in the healthcare sphere. Constructing an IDS that detects and returns information efficiently and with the highest accuracy is a challenging task. Machine Learning (ML) techniques have been effectively adopted in IDSs to detect network intruders. Reinforcement learning is considered as one of the main developments in ML. IDS mainly performs a higher accuracy rate, detection rate as well as a higher performance of a classification (ROC curve). According to these and to tackle the security issues, a Federated Reinforcement Learning-based Intrusion Detection System (FRL-IDS) in the Internet of Things (IoT) networks for healthcare infrastructures has been proposed. The proposed model has been evaluated and compared to a similar model (i.e. SVM system). The proposed model shows superiority over the SVM-steered IDS with accuracy and detection rates of ≈ 0.985 and ≈ 96.5%, respectively. This proposed infrastructure will not only aid in intrusion detection of large health care systems but also other wireless decentralized networks found across multiple real-world applications.