Topology Verification Enabled Intrusion Detection for In-Vehicle CAN-FD Networks
Bibliographic record
Abstract
Controller area network with flexible data rate (CAN-FD) is a widely used communication protocol for in-vehicle sensing and control. However, due to the lack of inherent security mechanisms, unauthorized devices could access the CAN-FD by embedding external intruding devices (XIDs) to in-vehicle networks. Malicious intrusion into CAN-FD can expose a compromised vehicle to significantly increased safety, security, and privacy related risks. To enhance the security of CAN-FD networks, a novel intrusion detection method based on verification of network topology is proposed, where XIDs can be reliably detected through a simple random walk based network topology construction and subsequent verification. When an intrusion is detected, a secure mode would be triggered to further protect the network from being attacked. Simulation results indicate that multiple XIDs can be accurately detected, while the increment in the number of XIDs from 1 to 8 can lead to the convergence time increasing from 48 to 102 steps in the powertrain subnet and from 189 to 416 steps in the body subnet.
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 | 0.000 | 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 | 0.001 | 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".