Internet of Medical Things (IoMT) represents an application of the Internet of Things, where health professionals perform remote analysis of physiological data collected using sensors that are associated with patients, allowing real-time and permanent monitoring of the patient's health condition and the detection of possible diseases at an early stage. However, the use of wireless communication for data transfer exposes this data to cyberattacks, and the sensitive and private nature of this data may represent a prime interest for attackers. The use of traditional security methods on equipment that is limited in terms of storage and computing capacity is ineffective. In this context, we have performed a comprehensive survey to investigate the use of the intrusion detection system based on machine learning (ML) for IoMT security. We presented the generic three-layer architecture of IoMT, the security requirement of IoMT security. We review the various threats that can affect IoMT security and identify the advantage, disadvantages, methods, and datasets used in each solution based on ML. Then we provide some challenges and limitations of applying ML on each layer of IoMT, which can serve as direction for future study.
翻译:医疗用物互联网(IOMT)代表了对物联网的应用,保健专业人员对利用与病人有关的传感器收集的生理数据进行远程分析,从而能够对病人的健康状况进行实时和长期监测,并在早期阶段发现可能的疾病,然而,使用无线通信进行数据传输,使这种数据受到网络攻击,而这些数据的敏感和私人性质可能代表攻击者的主要利益。对储存和计算能力有限的设备使用传统安全方法是无效的。在这方面,我们进行了全面调查,调查以机器学习为基础的入侵探测系统的使用情况。我们介绍了IOMT的通用三层结构,即IOMT的安全要求。我们审查了可能影响IOMT安全的各种威胁,并查明根据ML在每种解决方案中使用的优势、劣势、方法和数据集。然后,我们提出了一些对IOMT的每一层应用MMM的 ML的挑战和限制,这可以作为未来研究的方向。