Meta-IDS: Meta-Learning-Based Smart Intrusion Detection System for Internet of Medical Things (IoMT) Network
Umer Zukaib, Xiaohui Cui, Chengliang Zheng, Mir Hassan, Zhidong Shen
Abstract
The Internet of Medical Things (IoMT) plays a crucial role in advancing smart healthcare by facilitating the real-time collection and processing of medical data. These interconnected devices leverage Artificial Intelligence to assist practitioners in making data-driven decisions. However, IoMT’s dependence on communication protocols exposes it to significant security vulnerabilities. In response to this challenge, we propose a novel Meta-Intrusion Detection System (Meta-IDS) that employs a meta-learning approach to enhance the detection of both known and zero-day intrusions. Our approach seamlessly integrates signature-based and anomaly-based detection techniques, incorporating privacy-preserving methods essential for handling sensitive IoMT data. We rigorously evaluated our methodology using three publicly available datasets (WUSTL-EHMS-2020, IoTID20, and WUSTL-IIOT-2021). The results demonstrate remarkable accuracy rates of 99.57%, 99.93%, and 99.99% for signature-based detection, and 99.47%, 99.98%, and 99.99% for anomaly-based detection, coupled with impressively low misclassification rates of 0.0042%, 0.0006%, and 0.00004%, respectively. Through a comparative analysis with the state-of-the-art E-GraphSAGE model, considering metrics such as accuracy, precision, recall, F1-score, time complexity, and misclassification rate, we affirm the performance and reliability of the Meta-IDS. Our approach holds significant promise in bolstering cybersecurity within the IoMT network.