RCLNet:一种有效的基于异常的入侵检测,用于保护 IoMT 系统。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2024-10-03 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1467241
Jamshed Ali Shaikh, Chengliang Wang, Wajeeh Us Sima Muhammad, Muhammad Arshad, Muhammad Owais, Rana Othman Alnashwan, Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna
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引用次数: 0

摘要

医疗物联网(IoMT)通过远程患者监控和实时诊断彻底改变了医疗行业,但由于复杂的网络威胁和医疗信息的敏感性,患者数据的安全仍然是一项严峻的挑战。传统的机器学习方法难以捕捉 IoMT 数据中的复杂模式,而传统的入侵检测系统往往无法识别未知攻击,从而导致高误报率和患者数据安全受损。为了解决这些问题,我们提出了一种有效的基于异常的 IoMT 入侵检测系统(A-IDS)--RCLNet。RCLNet 采用了一种多方面的方法,包括用于特征选择的随机森林(RF)、用于增强模式识别的卷积神经网络(CNN)和长短期记忆(LSTM)模型的集成,以及专为应对 IoMT 独特挑战而设计的自适应注意层机制(SAALM)。此外,RCLNet 还利用焦点损失(FL)来管理不平衡的数据分布,这也是 IoMT 数据集中的一个常见挑战。使用 WUSTL-EHMS-2020 医疗保健数据集进行的评估表明,RCLNet 的性能优于最新的先进方法,准确率高达 99.78%,这突显了它在显著提高 IoMT 医疗保健系统中患者数据的安全性和保密性方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system.

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems.

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来源期刊
CiteScore
4.20
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审稿时长
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