Two-Layer Bus-Independent Instruction Set Architecture for Securing Long Protocol Data Units in Automotive Open System Architecture-Based Automotive Electronic Control Units
Bibliographic record
Abstract
In this paper, we propose a bus-independent hardware (HW)-based approach to secure long protocol data units (PDUs) in Automotive Open System Architecture (AUTOSAR)-based automotive electronic control units (ECUs). Our approach is based on extending previous works that implemented two AUTOSAR communication (COM) application-specific instruction set processors (ASIPs). COM ASIP V1 introduced two instructions to handle the transmission and reception of PDUs no larger than 8 bytes and signals no larger than 32 bits individually through send signal and receive signal instructions. COM ASIP V2 introduced two extra instructions to handle long signals and PDUs of arbitrary lengths. We extended the instruction set architecture (ISA) of our previous ASIPs by introducing six new instructions, in COM ASIP V3, to hash PDUs that contain these signals to authenticate transmission and reception of such PDUs. The experimental results show that COM ASIP V3 can handle (i.e., transmit, receive, calculate hash, or verify hash) a 64-byte controller area network flexible data-rate (CAN FD) frame in 1.575 μs and a 254-byte FlexRay frame in 6.301 μs. These measurements indicate that the throughput of our new COM ASIP is much higher, 42× to 75×, than the throughput required by these communication buses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".