Protection against Ransomware in Industrial Control Systems through Decentralization using Blockchain
Bibliographic record
Abstract
Industrial control systems (ICSs), such as Supervisory Control and Data Acquisition (SCADA) systems, are increasingly popular for manufacturing applications, leading to significant improvements in efficiency and productivity. However, the vulnerability of these systems to ransomware attacks has become a major concern. This vulnerability is mainly due to the centralized nature of ICSs, which prioritize efficiency over security. To address this issue, this paper proposes a decentralized Blockchain-Based ICS (BBICS) architecture. Such architecture uses a peer-to-peer network of nodes to replicate critical data and distribute transactions using a consensus mechanism, which synchronizes nodes and resolves single points of failure. Additionally, BBICS encrypts critical data in a tamper-resistant manner to prevent attackers from decrypting or manipulating data. Moreover, zero-trust authorization and authentication further enhance security by preventing the broadcasting of ransomware attacks in internal networks of devices. The evaluation of the proposed system with respect to performance and reliability under normal and ransomware attack situations suggest BBICS’ feasibility and practicality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".