Lightweight Authenticated Encryption for Vehicle Controller Area Network
Bibliographic record
Abstract
Vehicle manufacturers are installing a large number of Electronic Control Units (ECU) inside vehicles. ECUs communicate among themselves via a Controller Area Network (CAN) to ensure better user experience and safety. CAN is considered as a de facto standard for efficient communication of an embedded control system network. However, it has no built-in security features. In this thesis, the existing security solutions for the CAN protocol found in the literature are classified in terms of security enforcement procedures. The classification can facilitate the researchers to select an appropriate security technique depending on security requirements. We also propose a security framework to secure CAN communication using the Authenticated Encryption with Associated Data (AEAD). The framework ensures confidentiality, integrity, and authenticity of CAN data transmission. The experimental results show that the delay of the proposed approach can be reduced to 0.07 ms depending on hardware configurations. We consider it lightweight since it adds a low overhead regardless of performing encryption and authentication. We evaluate the approach using four metrics: communication overhead, network traffic load, cost of deployment, and compatibility with CAN specification. We show that the framework keeps the network traffic unchanged, has low deployment cost, and is highly compatible with the specifications of the protocol.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".