Entropy-Rich One-Time Password Generation Utilizing Sensors in a Hardware-Realized Chaotic Chua's Circuit
Bibliographic record
Abstract
This paper introduces a novel hardware-based solution for generating one-time passwords (OTPs) using a field-programmable gate array (FPGA). By leveraging real-world analog noise sources like light, temperature, and sound sensors, the system ensures a high level of entropy to seed the random number generation process in a dedicated FPGA chaotic Chua's circuit. The design of this OTP generator is capable of producing secure 5-digit OTPs ranging from 00000 to 99999. These OTPs can serve various purposes, such as wireless applications when transmitted to an ESP32 microcontroller or authentication in access control systems. By integrating these OTPs directly into access control systems, organizations can enhance their security measures significantly. This integration allows for seamless and secure authentication processes, ensuring that only authorized individuals gain access to restricted areas. The proposed approach prioritizes high randomness and resistance to prediction, essential characteristics for secure embedded systems. By incorporating multiple noise sources and utilizing FPGA technology, the OTP generator guarantees a robust level of security. Overall, the hardware-based OTP generator presented in this paper stands as a reliable and innovative solution for enhancing security in embedded systems.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".