Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan.
Sensors (Basel). 2024 May 24;24(11):3375. doi: 10.3390/s24113375.
The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.
医疗行业通过整合医疗物联网（IoMT）进行了改革，使数据能够通过来自不同设备的传输介质进行收集，包括患者到医疗保健人员设备，以便通过基于云的服务器进行进一步分析，从而对患者进行正确诊断，产生高效和准确的结果。然而，IoMT 技术在安全风险和漏洞方面存在一系列缺陷，例如侵犯和暴露患者敏感和机密数据。此外，网络流量数据容易受到无线通信和数据篡改引起的拦截攻击，这可能会导致不良后果。所提出的方案为 IoMT 网络提供了一种强大的入侵检测系统（IDS）。它利用蜜罐将攻击者从关键系统中转移开，从而减少攻击面。此外，IDS 采用了一种结合逻辑回归和 K-最近邻算法的集成方法。这种方法利用了两种算法的优势，以提高攻击检测的准确性和鲁棒性。这项工作分析了所使用模型对两个与 IoMT 相关的数据集的影响、性能、准确性和精度结果，这些数据集包含多种攻击类型，如中间人（MITM）、数据注入和分布式拒绝服务（DDOS）。结果表明，所提出的集成方法在检测入侵尝试并将其分类为攻击或正常网络流量方面非常有效，对于第一个数据集的准确率为 92.5%，对于第二个数据集的准确率为 99.54%，对于第一个数据集的精度为 96.74%，对于第二个数据集的精度为 99.228%。
