A Model-Based Method for Enabling Source Mapping and Intrusion Detection on Proprietary Can Bus
Bibliographic record
Abstract
With the deep integration of the Internet of Things (IoT) technology and the increase of computational power and memory, vehicles can also serve as the infrastructures for Intelligent Transportation System (ITS), e.g., as fog nodes. However, when connecting vehicles to the internet, alongside with the benefits it brings, it also opens many new challenges such as security attacks. Controller Area Network (CAN) is one of the main in-vehicle communication protocols in modern cars. Its lack of sender verification mechanism makes CAN particularly vulnerable to cyber-attacks including masquerade attack. Fingerprinting Electronic Control Units (ECUs) based on hardware characteristics has been proved feasible and effective on defending CAN buses. However, most state-of-the-art works exploited the supervised learning algorithm to identify the transmitter based on the signal characteristics. This makes the decision process hard to understand, and it also limits the deployment on proprietary CAN bus without prior knowledge. To solve this, we design a novel clock-skew-based approach capable of pinpointing the sender and detecting intrusion on proprietary CAN bus. We take a single CAN frame as the object for measurement, and adjust the measuring process such that our approach can be independent of the transmission time of frames. Based on the statistical analysis of data from real vehicles, we propose a novel box-plot algorithm based on score mechanism to filter the raw data. Finally, the clock skews are estimated and accumulated to build a linear model for representing the transmitter ECU. The evaluation results on one CAN prototype and two production vehicles show that our approach is able to well identify and differentiate ECUs on the bus without prior knowledge. The data processed by the proposed box-plot algorithm can describe the hardware characteristics of ECUs precisely. We also show the ability of our approach to protecting the CAN bus against the masquerade attack.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".