Modular Security Engine Cyber-Physical Resiliency Approach Using Digital Replication
Bibliographic record
Abstract
In this paper, we propose an IoT device cyber physical resiliency approach using a cross-domain device replication that can replicate and securely transfer the profile of the Internet of Things (IoT) user device to another. Our approach aims to offer a cybersecurity abstraction layer to offer continuation of the security engine service when the IoT user device is facing interruption of services in case of IoT-centric solutions, including IoT Mobile payment, Smart city networks communication services, safety communications with integrated location-based services. In a world of cybersecurity where any IoT user devices are vulnerable to hackers, existing IoT devices require them to remain efficiently powered, connected to carry end-to-end secure transaction in an IoT environment without failure. To achieve our goal of resilience and recoverability, we upgraded the IoT modular security engine features with digital replication functions and secure profile transfer capabilities to effectively complete started transactions in situations where lacking sufficient power to transmit or in case of an abrupt connection loss due to network handovers. This leads to integration of IoT cross-environment optimal security enhancements into the existing Simple Public Key Infrastructure following the Pretty Good Privacy Web of Trust approach with Secure Device Profile and Data Processing. Moreover, our implementation succeeded in providing a service continuation on the replicated IoT device, preserving and offering a resilient secure capable environment for carrying secure transactions. The results show a communication success rate of 90 percent between all Security Engine components (DSM, CNSM, SFB) called modules on replicated IoT devices while improving the simulation running time to run two times longer (30 seconds) in each round of tests with 10 per cent faster response and recovery time of 7 seconds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".