Leveraging Deep Learning for Adaptive Intrusion Prevention in Smart Devices
Bibliographic record
Abstract
Smart device usage during the age of IoT was more convenient than ever, but it opened new windows of opportunity for cyberattacks. Legacy IPS has not been able to prevent dynamic, sophisticated threats from smart devices. This paper proposes an adaptive intrusion-prevention method for smart devices based on deep learning. Researchers introduce a hybrid framework that leverages a Convolutional Neural Network (CNN) to extract spatial features from network traffic time series and an LSTM network to handle sequential data at varying time steps, enabling the system to learn and adapt to changing attack trends. It trains and tests on the "Smart Home Intrusion Detection Dataset," a publicly available Kaggle data set comprising a sequence of common smart home network attack scenarios. It is developed using TensorFlow and PyTorch, trending deep learning frameworks, with Scikit-learn library support for data pre-processing, post-processing, and metrics. Our results confirm that the proposed model is unmatched in accuracy for intrusion prevention and detection compared with traditional machine learning models. The deep learning model's ability to learn and optimise makes it a potential candidate for enhancing the security of smart devices against advanced cyberattacks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".