An efficient self attention-based 1D-CNN-LSTM network for IoT attack detection and identification using network traffic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the last ten years, the IoT has played a crucial role in society's digital transformation. However, because of the wide range of devices it encompasses, it is also facing increased security vulnerabilities. This research presents a novel mechanism called the self-attention-based 1D-CNN-LSTM, which uses convolutional neural networks (CNNs) combined with a long short-term memory (LSTM) model enhanced with a self-attention mechanism for detecting IoT attacks. The proposed mechanism achieves high accuracy and efficiently differentiates malicious and benign network traffic. By employing Shapley additive explanations (SHAP), we identified important predictive features from the preprocessed data, which were retrieved using CICFlowMeter. This has strengthened the dependability of the model. In addition, we enhanced the model by training it on a smaller collection of features, resulting in shorter training time while preserving accuracy. We have also generated nine augmented IoT tabular datasets named CIC-BCCC-NRC_TabularIoTAttack-2024 from accessible IoT datasets to evaluate the model's robustness and showcase its efficacy in IoT security.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it