增强型随机森林分类器与 K-Means 聚类(ERF-KMC)用于检测和预防医疗物联网网络中的分布式拒绝服务和中间人攻击

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Abdullah Ali Jawad Al-Abadi, M. Mohamed, Ahmed Fakhfakh
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引用次数: 0

摘要

近年来,无线人体传感器网络(WBSN)与医疗物联网(IoMT)的结合标志着医疗保健技术进入了一个变革时代。两者的结合实现了医疗设备之间的顺畅通信,从而能够实时监测病人的生命体征和健康参数。然而,连接性的增强也带来了安全挑战,特别是与攻击节点的存在有关的挑战。针对这些挑战,本文提出了一种独特的解决方案,即采用 K 均值聚类算法的增强型随机森林分类器(ERF-KMC)。所提出的 ERF-KMC 算法将实现最佳执行时间的增强型随机森林分类器(ERF-ABE)的准确性与 K-means 的聚类能力相结合。该模型发挥了双重作用。最初,通过使用 ERF-ABE 检测攻击信息来增强 IoMT 网络的安全性,随后使用 K-means 对攻击类型进行分类,特别是区分中间人(MITM)和分布式拒绝服务(DDoS)。这种方法有助于对攻击进行精确分类,使 ERF-KMC 算法能够采用适当的方法有效阻止这些攻击信息。随后,这种方法有助于改善攻击期间明显恶化的网络性能指标,包括数据包丢失率(PLR)、端到端延迟(E2ED)和吞吐量。这是通过检测攻击节点并随后阻止其进入 IoMT 网络来实现的,从而减轻了潜在的破坏并提高了整体网络效率。本研究使用 Python 编程语言进行了模拟,以评估 ERF-KMC 算法在 IoMT 领域的性能,尤其侧重于网络性能指标。与其他算法相比,ERF-KMC 算法显示出卓越的功效,与网络安全领域的其他常见算法(如 AdaBoost、CatBoost 和随机森林)相比,ERF-KMC 算法在优化 IoMT 网络性能方面具有更强的能力。ERF-KMC 算法的重要性在于其对 IoMT 网络的安全性,因为它为识别和预防 MITM 和 DDoS 攻击提供了一种高安全性方法。此外,改进网络性能指标以确保传输的医疗数据准确高效,对于实时监控病人至关重要。这项研究为提高 IoMT 系统的可靠性和安全性、推动未来互联医疗技术的发展迈出了新的一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks
In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s vital signs and health parameters. However, the increased connectivity also introduced security challenges, particularly as they related to the presence of attack nodes. This paper proposed a unique solution, an enhanced random forest classifier with a K-means clustering (ERF-KMC) algorithm, in response to these challenges. The proposed ERF-KMC algorithm combined the accuracy of the enhanced random forest classifier for achieving the best execution time (ERF-ABE) with the clustering capabilities of K-means. This model played a dual role. Initially, the security in IoMT networks was enhanced through the detection of attack messages using ERF-ABE, followed by the classification of attack types, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) using K-means. This approach facilitated the precise categorization of attacks, enabling the ERF-KMC algorithm to employ appropriate methods for blocking these attack messages effectively. Subsequently, this approach contributed to the improvement of network performance metrics that significantly deteriorated during the attack, including the packet loss rate (PLR), end-to-end delay (E2ED), and throughput. This was achieved through the detection of attack nodes and the subsequent prevention of their entry into the IoMT networks, thereby mitigating potential disruptions and enhancing the overall network efficiency. This study conducted simulations using the Python programming language to assess the performance of the ERF-KMC algorithm in the realm of IoMT, specifically focusing on network performance metrics. In comparison with other algorithms, the ERF-KMC algorithm demonstrated superior efficacy, showcasing its heightened capability in terms of optimizing IoMT network performance as compared to other common algorithms in network security, such as AdaBoost, CatBoost, and random forest. The importance of the ERF-KMC algorithm lies in its security for IoMT networks, as it provides a high-security approach for identifying and preventing MITM and DDoS attacks. Furthermore, improving the network performance metrics to ensure transmitted medical data are accurate and efficient is vital for real-time patient monitoring. This study takes the next step towards enhancing the reliability and security of IoMT systems and advancing the future of connected healthcare technologies.
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来源期刊
Computers
Computers COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.40
自引率
3.60%
发文量
153
审稿时长
11 weeks
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