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一种用于智能医疗安全的基于智能注意力的深度卷积学习（IADCL）模型。

An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security.

作者信息

Maruthupandi J, Sivakumar S, Dhevi B Lakshmi, Prasanna S, Priya R Karpaga, Selvarajan Shitharth

机构信息

Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India.

Department of Computer Science and Engineering, SRM Institute of Science and Technology, Trichy, Tamilnadu, India.

出版信息

Sci Rep. 2025 Jan 8;15(1):1363. doi: 10.1038/s41598-024-84691-8.

DOI:10.1038/s41598-024-84691-8
PMID:39779774
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11711617/
Abstract

In recent times, there has been rapid growth of technologies that have enabled smart infrastructures-IoT-powered smart grids, cities, and healthcare systems. But these resource-constrained IoT devices cannot be protected by existing security mechanisms against emerging cyber threats. The aim of the paper is to present an improved security for smart healthcare IoT systems by developing an architecture for IADCL. The proposed system employs publicly available datasets such as CIC-IDS 2017, CIC-IDS 2018, CIC-Bell DNS 2021, and NSL-KDD to present a robust detection framework. IRKO selects features, reducing the feature dimensions and hence isolating the most relevant attributes. The AConBN classifier then accurately classifies normal and intrusion traffic. Afterwards, optimization in the classification process is done by the SA-HHO algorithm, which provides the optimal weight values. Results are such that the IADCL framework detects cyberattacks with a high degree of accuracy, and the performance evaluations are made based on a number of key performance metrics. Conclusively, the proposed system has very good potential to protect smart healthcare IoT devices from cyber threats.

摘要

近年来，能够实现智能基础设施的技术迅速发展，如物联网驱动的智能电网、城市和医疗系统。但这些资源受限的物联网设备无法通过现有安全机制抵御新出现的网络威胁。本文的目的是通过开发一种IADCL架构，为智能医疗物联网系统提供改进的安全性。所提出的系统使用诸如CIC-IDS 2017、CIC-IDS 2018、CIC-Bell DNS 2021和NSL-KDD等公开可用的数据集，以呈现一个强大的检测框架。IRKO选择特征，减少特征维度，从而分离出最相关的属性。然后，AConBN分类器对正常流量和入侵流量进行准确分类。之后，通过SA-HHO算法对分类过程进行优化，该算法提供最优权重值。结果表明，IADCL框架能够高度准确地检测网络攻击，并基于一些关键性能指标进行性能评估。总之，所提出的系统在保护智能医疗物联网设备免受网络威胁方面具有很大的潜力。

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