Using Authenticated Encryption for Securing Controller Area Networks in Autonomous Mobile Platforms
Bibliographic record
Abstract
Controller Area Networks (CAN) has become the dominant data communication and networking system used in today's smart vehicles, autonomous mobile platforms, and industrial control systems. Although CAN bus, a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other's applications provides robust and reliable internode data communication, existing CAN technology lacks support for secure data communication and data authentication. This paper explores the utilization of a lightweight and power-efficient authenticated data encryption engine based on TinyJAMBU-128. The proposed system is suitable for autonomous mobile platforms with CAN bus capability. In our proposed testbed, CAN data frames transmitted over the bus will be encrypted and authenticated with minimal power consumption using TinyJAMBU-128. We have analyzed the performance of TinyJAMBU-128 against three parameterized data encryption modules: AES-128/192/256, CAMELLIA-128/192/256, and ARIA-128/192/256. Based on our simulation data, CAN frames encrypted and authenticated via TinyJAMBU-128 required 22%, 17%, and 15% less average energy consumption compared to AES, CAMELLIA, and ARIA respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".