A Quantum-Based Signcryption for Supervisory Control and Data Acquisition (SCADA) Networks
Bibliographic record
Abstract
Supervisory Control and Data Acquisition (SCADA) systems are ubiquitous in industrial control processes, such as power grids, water supply systems, traffic control, oil and natural gas mining, space stations and nuclear plants. However, their security faces the threat of being compromised due to the increasing use of open-access networks. Furthermore, one of the research gaps involves the emergence of quantum computing, which has exposed a new type of risk to SCADA systems. Failure to secure SCADA systems can lead to catastrophic consequences. For example, a malicious attack can take control of the power supply to a city, shut down the water supply system, or cause malfunction of a nuclear reactor. The primary purpose of this paper is to identify the new type of attack based on quantum computing and design a novel security scheme to defend against traditional attacks as well as the quantum attack. The methodology of the proposed signcryption is built on the foundation of the classical Bennett and Brassard 1984 (BB84) cryptographic scheme and does not involve computationally expensive third-party validation. The proposed signcryption scheme provides both encryption and intrusion detection. In particular, it detects the man-in-the-middle attack that can lead to other types of attacks. We have simulated the proposed algorithm using the Quantum Information Toolkit in Python. Furthermore, we have validated and analyzed the proposed design through security verification tools, namely, Scyther and PRISM.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".