Efficient Design for a Hardware Implementation of the LED Block Cipher
Bibliographic record
Abstract
Recently, most modern cars can be controlled with the Remote Keyless System (RKS).A Remote Keyless System consists of a key fob that communicates wirelessly with the car transceiver that is used to control and secure access to the vehicle.The keyless entry systems are based on RFID communication and are equipped with limited computation and power resources.It was proven to be a target for cybercriminals.In this work, we proposed the use of the LED block cipher for data encryption-decryption of the keyless entry systems.The proposed hardware architecture of the LED algorithm was optimized to fit the limited resources of the keyless entry systems.The proposed 8-bit sequential architecture contains 627 LUTs+FFs.To further reduce hardware resources, we proposed a 4-bit architecture for the MixColumns sub-function, which gives good results in occupying hardware resources by reducing it to 597 LUTs+FFs.This leads to a good effect on the area implementation and power consumption of keyless entry systems.Thus, the proposed LED hardware architecture may be applied to lightweight applications that demand a high level of secrecy such as the key fob.In order to adopt the proposed design of the LED block cipher as a security system for keyless entry systems, we examine the security of the proposed LED architecture using five metrics; entropy analysis, histogram analysis, correlation analysis, NPCR, and UACI.As a result, the LED block cipher has a good ability to encrypt any data against any attack.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".