Machine Learning-Based Intrusion Detection System for Controller Area Networks
Bibliographic record
Abstract
The automotive industry continues to innovate at an exponential rate to provide a safer and more efficient experience for consumers. Autonomous vehicles and Vehicle-to-Everything technologies are at the forefront of defining the future of transportation. Enabling vehicles to connect to various services has exposed critical in-vehicle networks such as the Controller Area Network (CAN) to potential exploitation by adversaries. In its standard form, the CAN bus suffers from multiple vulnerabilities such as limited bandwidth and lack of authentication. Attacks can be initiated through physical and wireless mediums, exploiting diagnostic interfaces, Bluetooth and infotainment systems to compromise the confidentiality, integrity and availability of data communication within vehicles. In this paper, a holistic, comprehensive, Machine Learning-Based intrusion detection system for the CAN bus is proposed to secure the critical in-vehicle network. The proposed system is modular, scalable and can be adapted to the ever-changing threat landscape of cyber vehicle attacks. On an unseen testing dataset, our system achieved 100% accuracy in protecting against denial of service and multiple impersonation injection attacks, as well as 95.67% accuracy of fuzzy injection attacks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".