Meta-IDS: Meta-Learning-Based Smart Intrusion Detection System for Internet of Medical Things (IoMT) Network

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'...

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Vydáno v:IEEE internet of things journal Ročník 11; číslo 13; s. 23080 - 23095
Hlavní autoři: Zukaib, Umer, Cui, Xiaohui, Zheng, Chengliang, Hassan, Mir, Shen, Zhidong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Anomaly detection
Artificial intelligence
artificial intelligence (AI)
Computer crime
Cybersecurity
Internet of Medical Things
Internet of Medical Things (IoMT)
Intrusion detection
Intrusion detection systems
intrusion detection systems (IDSs)
Learning
meta learning
Metalearning
Monitoring
Real time
Security
Sensors
zero-day attacks
ISSN:2327-4662, 2327-4662
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Shrnutí: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 data sets (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.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3387294