Purpose

This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

Design/methodology/approach

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

Findings

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

Originality/value

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

中文翻译：

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使用优化深度学习的医疗物联网攻击检测：增强医疗保健领域的安全性

目的

本文使用改进的深度学习架构在医疗物联网 (IoT) 设备中实施攻击检测的策略，以实现自带设备 (BYOD) 的概念。在这里，对基于仿真的医院环境进行建模，其中许多物联网设备或医疗设备相互通信。在属性收集的支持下识别发起攻击的节点或设备。与医疗物联网中的攻击检测有关的数据集是从被视为特征的每个节点收集的。这些特征受制于深度信念网络 (DBN)，这是深度学习算法的一部分。尽管现有 DBN，在混合元启发式算法的帮助下，通过合并蚱蜢优化算法（GOA）和蜘蛛猴优化（SMO），正确调整或优化 DBN 的隐藏神经元数量，以提高检测的准确性。混合算法被称为基于本地领导阶段的 GOA (LLP-GOA)。DBN 用于通过创建包含攻击细节的数据库来训练节点，从而在测试过程中保持准确的检测。

设计/方法/方法

本文介绍了使用改进的深度学习架构作为 BYOD 的医疗物联网设备中的新型攻击检测。因此，本文旨在展示医院网络中检测攻击的高收敛性和更好的性能。

发现

从分析来看，所提出的基于LLP-GOA的DBN在准确性方面的整体性能分析比粒子群优化（PSO）-DBN好0.25％，比灰狼算法（GWO）-DBN提高0.15％，0.26％比 SMO-DBN 增强，比 GOA-DBN 增强 0.43%。同样，所提出的 LLP-GOA-DBN 模型的准确率比支持向量机 (SVM) 高 13%，比 k-最近邻 (KNN) 高 5.4%，比神经网络 (NN) 高 8.7% 和 3.5%比 DBN。

原创性/价值

本文采用称为 LLP-GOA 的混合算法来准确检测医疗物联网中的攻击，以使用优化的深度学习提高医疗保健领域的安全性。这是第一项利用 LLP-GOA 算法提高 DBN 性能以增强医疗保健领域安全性的工作。