Naik Nithesh, Surendranath Neha, Raju Sai Annamaiah Basava, Madduri Chennaiah, Dasari Nagaraju, Shukla Vinod Kumar, Patil Vathsala
Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Engineering, LinkedIn, Mountain View, CA, 94043, USA.
Sci Rep. 2025 Aug 23;15(1):31039. doi: 10.1038/s41598-025-15292-2.
The increasing reliance on Human-centric Internet of Things (H-IoT) systems in healthcare and smart environments has raised critical concerns regarding data integrity, real-time anomaly detection, and adaptive access control. Traditional security mechanisms lack dynamic adaptability to streaming multimodal physiological data, making them ineffective in safeguarding H-IoT devices against evolving threats and tampering. This paper proposes a novel trust-aware hybrid framework integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models, and Variational Autoencoders (VAE) to analyze spatial, temporal, and latent characteristics of physiological signals. A dynamic Trust-Aware Controller (TAC) is introduced to compute real-time trust scores using anomaly likelihood, context entropy, and historical behavior. Access decisions are enforced via threshold-based logic with a quarantine mechanism. The system is evaluated on benchmark datasets and proprietary H-IoT signals under diverse attack and noise scenarios. Experiments are conducted on edge devices including Raspberry Pi and Jetson Nano to assess scalability. The proposed framework achieved an average F1-score of 94.3% for anomaly detection and a 96.1% accuracy in access decision classification. Comparative results against rule-based and statistical baselines showed a 12-18% improvement in detection sensitivity. Real-time inference latency was maintained under 160 ms on edge hardware, validating feasibility for critical H-IoT deployments. Trust scores exhibited high stability under adversarial data fluctuations. This research delivers a scientifically grounded, practically scalable solution for adaptive security in H-IoT networks. Its novel fusion of deep learning and trust modeling enhances both responsiveness and resilience, paving the way for next-generation secure health and wearable ecosystems.
医疗保健和智能环境中对以人类为中心的物联网（H-IoT）系统的依赖日益增加，引发了对数据完整性、实时异常检测和自适应访问控制的严重关切。传统的安全机制缺乏对流式多模态生理数据的动态适应性，使其在保护H-IoT设备免受不断演变的威胁和篡改方面无效。本文提出了一种新颖的信任感知混合框架，该框架集成了卷积神经网络（CNN）、长短期记忆（LSTM）模型和变分自编码器（VAE），以分析生理信号的空间、时间和潜在特征。引入了动态信任感知控制器（TAC），使用异常可能性、上下文熵和历史行为来计算实时信任分数。通过基于阈值的逻辑和隔离机制来执行访问决策。该系统在基准数据集和专有H-IoT信号上进行了不同攻击和噪声场景下的评估。在包括树莓派和英伟达Jetson Nano在内的边缘设备上进行了实验，以评估其可扩展性。所提出的框架在异常检测方面的平均F1分数达到了94.3%，在访问决策分类方面的准确率达到了96.1%。与基于规则和统计基线的比较结果显示，检测灵敏度提高了12-18%。边缘硬件上的实时推理延迟保持在160毫秒以下，验证了关键H-IoT部署的可行性。信任分数在对抗性数据波动下表现出高稳定性。这项研究为H-IoT网络中的自适应安全提供了一个科学合理、实际可扩展的解决方案。其深度学习与信任建模的新颖融合增强了响应能力和恢复能力，为下一代安全健康和可穿戴生态系统铺平了道路。
