Deep Learning Based Cyber Security Enhancement for Anomaly Detection in IoT Based Healthcare Data Security
Bibliographic record
Abstract
The proliferation of internet of things (IoT) in the healthcare domain has enabled real-time patient monitoring, automated diagnostics, and improved medical decision-making. However, this digital transformation has simultaneously introduced new security vulnerabilities, making healthcare IoT (H-IoT) systems highly susceptible to cyber attacks and anomalous behaviors. Traditional intrusion detection systems suffer from high false positive rates, limited scalability, and poor generalization in real-world scenarios. The objective of this work is to design a robust deep learning-based anomaly detection framework that enhances cyber security in H-IoT systems by minimizing false alarms, optimizing detection accuracy, and reducing computational overhead through effective feature optimization and classification. A hybrid detection model is proposed, integrating an enhanced social group optimization (ESGO) algorithm for optimal feature selection and a multi-objective deep neural network (MO-DNN) for anomaly detection. The proposed model is trained and validated using the Canadian Institute for Cyber security (CIC) IoT dataset, which includes wide range of IoT-based attack scenarios. The model achieves a detection accuracy of 99.77%, precision of 98.8%, recall of 99.74%, and F1-score of 99.77%, indicating its effectiveness in identifying complex attack patterns while maintaining low false positive rates.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".