Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".