Alalwany Easa, Alsharif Bader, Alotaibi Yazeed, Alfahaid Abdullah, Mahgoub Imad, Ilyas Mohammad
College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.
Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.
Sensors (Basel). 2025 Jan 22;25(3):624. doi: 10.3390/s25030624.
The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks. The proposed IDS leverages machine learning (ML) and deep learning (DL) techniques, employing a stacking ensemble method to enhance detection accuracy by integrating the strengths of multiple classifiers. To ensure real-time performance, the IDS is implemented within a Kappa Architecture framework, enabling continuous processing of IoMT data streams. The system effectively detects and classifies a wide range of cyberattacks, including ARP spoofing, DoS, Smurf, and Port Scan, achieving an outstanding detection accuracy of 0.991 in binary classification and 0.993 in multi-class classification. This research highlights the potential of combining advanced ML and DL methods with ensemble learning to address the unique cybersecurity challenges of IoMT systems, providing a reliable and scalable solution for safeguarding healthcare services.
医疗物联网（IoMT）正在通过互联的医疗设备和系统实现先进的患者护理，从而彻底改变医疗保健行业。然而，其关键作用和敏感数据使其成为网络威胁的主要目标，需要实施有效的安全解决方案。本文提出了一种专门为IoMT网络设计的新型入侵检测系统（IDS）。所提出的IDS利用机器学习（ML）和深度学习（DL）技术，采用堆叠集成方法，通过整合多个分类器的优势来提高检测准确率。为确保实时性能，IDS在Kappa架构框架内实现，能够持续处理IoMT数据流。该系统有效地检测和分类了各种网络攻击，包括ARP欺骗、拒绝服务、Smurf攻击和端口扫描，在二分类中实现了0.991的出色检测准确率，在多分类中实现了0.993的检测准确率。本研究突出了将先进的ML和DL方法与集成学习相结合以应对IoMT系统独特的网络安全挑战的潜力，为保障医疗服务提供了可靠且可扩展的解决方案。
