Jayanthi S, Suhasini Sodagudi, Sharmili N, Laxmi Lydia E, Shwetha V, Dash Bibhuti Bhusan, Bachute Mrinal
Department of Artificial Intelligence & Data Science, Faculty of Science and Technology (IcfaiTech), The ICFAI Foundation for Higher Education (IFHE), Hyderabad, Telangana, 01 503, India.
Department of Information Technology, Siddhartha Academy of Higher Education, (Deemed to Be University), Kanuru, Vijayawada-07, AP, India.
Sci Rep. 2025 Jul 2;15(1):22909. doi: 10.1038/s41598-025-05850-z.
Internet of Health Things (IoHT) plays a vital role in everyday routine by giving electronic healthcare services and the ability to improve patient care quality. IoHT applications and devices become widely susceptible to cyber-attacks as the tools are smaller and varied. Additionally, it is of dual significance once IoHT contains tools applied in the healthcare field. In the context of smart cities, IoHT enables proactive health management, remote diagnostics, and continuous patient monitoring. Therefore, it is essential to advance a strong cyber-attack detection method in the IoHT environments to mitigate security risks and prevent devices from being vulnerable to cyber-attacks. So, improving an intrusion detection system (IDS) for attack identification and detection using the IoHT method is fundamentally necessary. Deep learning (DL) has recently been applied in attack detection because it can remove and learn deeper features of known attacks and identify unknown attacks by analyzing network traffic for anomalous patterns. This study presents a Securing Attack Detection through Deep Belief Networks and an Advanced Metaheuristic Optimization Algorithm (SADDBN-AMOA) model in smart city-based IoHT networks. The main aim of the SADDBN-AMOA technique is to provide a resilient attack detection method in the IoHT environment of smart cities to mitigate security threats. The data pre-processing phase applies the Z-score normalization method for converting input data into a structured pattern. For the selection of the feature process, the proposed SADDBN-AMOA model designs a slime mould optimization (SMO) model to select the most related features from the data. Followed by the deep belief network (DBN) method is used for the attack classification method. Finally, the improved Harris Hawk optimization (IHHO) approach fine-tunes the hyperparameter values of the DBN method, leading to superior classification performances. The effectiveness of the SADDBN-AMOA method is investigated under the IoT healthcare security dataset. The experimental validation of the SADDBN-AMOA method illustrated a superior accuracy value of 98.71% over existing models.
健康物联网（IoHT）通过提供电子医疗服务以及提高患者护理质量的能力，在日常生活中发挥着至关重要的作用。由于IoHT应用程序和设备体积更小且种类繁多，它们极易受到网络攻击。此外，一旦IoHT包含应用于医疗领域的工具，其意义就变得双重。在智慧城市的背景下，IoHT可实现主动健康管理、远程诊断和患者持续监测。因此，在IoHT环境中推进一种强大的网络攻击检测方法以降低安全风险并防止设备易受网络攻击至关重要。所以，从根本上来说，改进一种使用IoHT方法进行攻击识别和检测的入侵检测系统（IDS）是必要的。深度学习（DL）最近已应用于攻击检测，因为它可以去除并学习已知攻击的更深层次特征，并通过分析网络流量中的异常模式来识别未知攻击。本研究提出了一种基于深度信念网络和先进元启发式优化算法的智慧城市IoHT网络安全攻击检测（SADDBN - AMOA）模型。SADDBN - AMOA技术的主要目标是在智慧城市的IoHT环境中提供一种弹性攻击检测方法，以减轻安全威胁。数据预处理阶段应用Z分数归一化方法将输入数据转换为结构化模式。对于特征选择过程，所提出的SADDBN - AMOA模型设计了一种黏液霉菌优化（SMO）模型，从数据中选择最相关的特征。随后，使用深度信念网络（DBN）方法进行攻击分类。最后，改进的哈里斯鹰优化（IHHO）方法对DBN方法的超参数值进行微调，从而实现卓越的分类性能。在物联网医疗安全数据集下研究了SADDBN - AMOA方法的有效性。SADDBN - AMOA方法的实验验证表明，其准确率高达98.71%，优于现有模型。
Sci Rep. 2025-7-1
Sci Rep. 2025-7-1
IEEE J Biomed Health Inform. 2023-10-19
Comput Intell Neurosci. 2022
Zotero 插件