Anomaly Detection Methodology of In-vehicle Network Based on Graph Pattern Matching
Bibliographic record
Abstract
Vehicles are becoming more and more connected today, with many direct interfaces and infotainment units widely deployed in in-vehicle networks. However, the increase in interfaces and units can also lead to an increase in cyberattacks surfaces. As the de facto standard for the in-vehicle network protocol, the Controller Area Network (CAN) protocol provides an efficient, stable, and cost-effective communication channel between electric control units (ECUs). Nonetheless, it is increasingly threatened by cyberattack due to the lack of security mechanisms by design. This paper proposes a novel anomaly detection methodology based on graph pattern matching, which expresses CAN traffic in terms of graph structures. Given a base graph and window graph, we determine whether the window graph represents normal or anomaly by using the distance measure on the base graph. We have validated this anomaly detection methodology on public datasets and in an actual vehicle environment. Experimental results show that this methodology significantly improved the detection of unknown attacks and outperforms other CAN traffic-based approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".