Attack Detection Using a Lightweight Blockchain Based Elliptic Curve Digital Signature Algorithm in Cyber Systems
Bibliographic record
Abstract
Cyber-physical systems (CPSs) are highly susceptible to malicious cyberattacks due to their reliance on communication networks. For this reason, many different attack detection techniques have been developed to guarantee the safety of CPSs. This article introduces BlockChain (BC) to address CPS issues such as data security and privacy. Additionally, BC is not well suited for CPS due to its high computing complexity, limited scalability, significant bandwidth overhead, and latency. To meet the requirements of CPS, a light-weight blockchain-based signature algorithm (LWBSA) model is developed in this work. The concept's resource constraints are alleviated by having a single centrally managed manager generate shared keys for outward-bound data transmission requests. The LWBSA paradigm provided herein produces an overlay network where extremely equipped resources can merge into a community BC, hence ensuring both dedicated privileges. Lightweight consensus, the elliptic curve digital signature algorithm (ECDSA), and distributed throughput management (DTM) are the three optimizations implemented in the ELIB model discussed here. Extensive simulation is carried out to examine the implications of different situations on processing time, energy usage, and overhead. The experimental outcomes show that the LWBSA achieves the best possible performance across a wide variety of measures.
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 | 0.001 | 0.003 |
| 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 | 0.002 |
| 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".