Signature-Over-The-Air with Transfer Learning IDS for Intelligent Connected Vehicles (ICV)
Bibliographic record
Abstract
Intelligent Connected Vehicles (ICV) are considered the promising technology that will replace traditional vehicles soon, especially with the improvement in automation and network technologies. This gives rise to the possibility of new cyberattacks, including intra- and inter-vehicle communication attacks. This paper proposed an infrastructure-independent, Intrusion Detection System (IDS) to secure the Intra-Vehicles and external networks. The anomaly-based IDS in the model is assisted by a blacklist attacks signatures that are placed in the connected vehicles, where the signatures of the new attacks can be generated in a cloud-based security management system and updated to the network of connected vehicles using the over-the-air-update concept, so each network-connected vehicle will work as a packet inspector that helps to find the attacks log and add it to a cloud signature database. In the detection engine, Self-taught Transfer Learning (STL) is used to transfer the knowledge of a pre-trained Deep Belief Network (DBN) model from the source domain and construct a feed-forward Deep Neural Network (DNN). The comparison with baseline machine learning (ML)/deep learning (DL) algorithms shows that the proposed model achieves better performance in terms of Accuracy, Precision, Detection Rate (DR), F1-score, and Receiver Operating Characteristic (ROC).
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".