RAPUF: A Novel Integration of Reversible Logic and Arbiter Physical Unclonable Functions for Enhancing IoT Security
Bibliographic record
Abstract
The Internet of Things (IoT) devices’ proliferation underscores the critical importance of fortifying nanoscale circuit security against evolving cyber threats. This paper introduces an unprecedented paradigm to enhance IoT security through the synergistic integration of reversible logic gates and Arbiter Physical Unclonable Functions (APUFs). Reversible logic, known for its theoretical efficiency and energy conservation, is coupled with APUFs, leveraging their unique ability to exploit nanoscale manufacturing variations for robust hardware security. The novel integration is specifically tailored to address the distinctive security challenges prevalent in IoT ecosystems. The proposed methodology encompasses designing and implementing a secure nanoscale circuit system, tailored to the resource constraints of IoT devices. Through an exhaustive literature review, we establish the significance of reversible logic and APUFs in the broader context of nanoscale circuit design and IoT security. The experimental results demonstrate not only the feasibility of the integrated approach but also its effectiveness in fortifying IoT devices against unauthorized access and data breaches. This research contributes a fresh perspective to the field of IoT security, presenting a novel solution that combines energy-efficient reversible logic with the intrinsic security features of APUFs. The outcomes of this work hold promise for advancing the security paradigm of IoT devices ensuring their resilience against emerging threats in the ever-evolving landscape of cybersecurity and contributing significantly to the evolving field of quantum-enhanced IoT security.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".