SCADA security issues and FPGA implementation of AES — A review
Bibliographic record
Abstract
Cyber-attacks from terrorist, national enemies, disgruntled employees are on the rise now on an Oil Refineries, on shore petroleum fields, off-shore Platforms, Oil and Gas Pipe Lines which will have a catastrophic impact on oil production and in turn on economy of the country, it can also cause serious damage to the environment living being, and even human lives. There is a dire need to protect Petroleum Oil &Gas Processing Infrastructures, Chemical Industry, Nuclear Power Stations, Water pumping & Waste treatment Plants, Electric Power Grids by using a Data Security System, similar systems are in place in Australia, Canada, America and other countries. Current trend of SCADA system protection is to perform backups, upgrade incremental capabilities each year without impacting 24/7 operations and train the personnel regularly, whenever up gradation is carried out. SCADA system should be operated over a Utility Intranet and isolated from public Internet by means of Firewalls, and Routers. It should have a protection based on dynamic predictive mechanism rather than reactive.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".