CSKES: A Context-Based Secure Keyless Entry System
Bibliographic record
Abstract
Remote keyless entry has been widely used on access control systems. These systems, in particular, Passive Keyless Entry and Start systems (PKES), allow drivers automatically unlock their vehicles by standing within one meter of the vehicle while carrying a key fob. Traditional key fobs adopt the RFID (Radio-Frequency IDentification) wireless communication technology. Yet, due to the restricted processing capacity of the key fobs and the vulnerabilities of RFID technology, these systems are subject to relay attack. In this paper, we propose a Context-based Secure Keyless Entry System (CSKES) that adopts BLE (Blue-tooth Low Energy) as a wireless communication technology and utilizes multiple context-based physical security features, namely, RSSI (Receiving Signal Strength Indicator), RTT (Round-Trip Time), GPS (Global Positioning System) coordinates, and Wi-Fi access point lists, to precisely identify the close proximity of a vehicle to its corresponding key fob. This multi-feature proximity identification system is highly efficient to mitigate classic relay attacks. We first introduce the implementation of the proposed system. Then we evaluate the system performance using three classification models with a dataset collected from normal and abnormal use cases. The results show that the proposed Context-based Secure Keyless Entry System demonstrates great efficiency in identifying physical proximity and preventing classic relay 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".