RSM: A Real-time Security Monitoring Platform for IoT Networks
Bibliographic record
Abstract
The rapid growth of Internet of Things (IoT) resulted in a heightened risk of security breaches, as cybercriminals have begun to target IoT devices and networks with increasingly sophisticated techniques. However, IoT security monitoring platforms face several challenges, including the inability to identify unknown threats, limited real-time prediction capabilities depending on signature-based threat identification, and the need for standardization and integration issues. In this paper, we propose a Real-Time Security Monitoring (RSM) platform based on the results of Deep Learning models, which can predict attacks on IoT networks and visualize the prediction results in a custom-built Power BI dashboard in a real-time manner. To evaluate our proposed solutions, we compare the effectiveness of three deep learning models - Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN) - using the IoT23 dataset in the context of the binary classification problem. We compare these models based on their accuracy, precision, recall, and F1 score. In addition, our findings show that our proposed platform outperforms existing solutions in terms of accuracy and can predict IoT network attacks with high precision and recall. We also implemented a test bed using a Raspberry PI programmed to send its logs to the nearest connected edge router and a server programmed using Python with a scheduler to pull those logs and show real-time Deep Learning Model prediction results in a Power BI dashboard. Our results demonstrate that the RSM and the Power BI dashboard provide a user-friendly way to monitor IoT Network security in real-time. This study provides valuable insights into applying Deep Learning (DL) and Power BI dashboard in the IoT security monitoring domain.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".