IoTBChain: Adopting Blockchain Technology to Increase PLC Resilience in an IoT Environment
Bibliographic record
Abstract
The networks on a centralized cloud architecture that interconnect Internet of Things (IoT) gadgets are not limited by national or jurisdictional borders. To ensure the secure sharing of sensitive user data among IoT gadgets, it is imperative to maintain security, resilience and trustless authentication. As a result, blockchain technology has become a viable option to provide such noteworthy characteristics. Blockchain technology is foundational for resolving many IoT security and privacy issues. Blockchain’s safe decentralization can solve the IoT ecosystem’s security, authentication and maintenance constraints. However, blockchain, like any innovation, has drawbacks, mainly when used in crucial IoT systems such as programmable logic controller (PLC) networks. This paper addresses the most recent security and privacy issues relating to the IoT, including the perception, network and application layers of the IoT’s tiered architecture. The key focus is to review the existing IoT security and privacy concerns and how blockchain might be used to deal with these problems. This paper proposes a novel approach focusing on IoT capabilities and PLC device security. The new model will incorporate a proof-of-work-based blockchain into the (PLC) IoT ecosystem. This blockchain enables the transmission of binary data and the data logging of the (PLC) networks’ signals. This novel technique uses fewer resources than other sophisticated methods in that PLC devices communicate data while maintaining a high transmission, encryption and decoding speed. In addition to ensuring repeatability, our new model addresses the memory and tracing problems that different PLC manufacturers encounter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".