Recommending Location for Placing Data Collector in the OPC Classic
Bibliographic record
Abstract
Industrial automation requires exchange of data from various systems. To enable interoperability, OPC (Open Platform Communications) has been used as interfacing standard to avoid direct interaction with internal architectures. In its nascent, OPC classic devices are based on the Microsoft COM/DCOM and RPC (Remote Procedure Call) technologies and thus inherit many of their security vulnerabilities. Therefore, it is essential to secure OPC classic and no previous work has contributed to comparative study of data collection on OPC-classic client, server, network, and SCADA to the best of our knowledge. In this paper, we propose various OPC classic architectures' data collection systems by designing several use cases using OPC classic client, server, and SCADA simulators. We use two OPC classic sniffers for data collection. We also benchmark the use cases in terms of CPU and memory utilization to suggest the best location for placing the data collector. We observed that memory resource utilization at the client side is the lowest, hence it is the best location for data collection. However, if the client has a CPU resource constraint, then data should be collected at the server side.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".