Sniffing Serial-Based Substation Devices: A Complement to Security-Centric Data Collection
Bibliographic record
Abstract
One of the key aids in ensuring security in the substation is data collection. Collected substation data can be applied to data analytics to infer the substation's security status. However, most of the data collected are measurement logs and Ethernet-based traffic data. The industrial network market share shows that serial-based protocols still exist, and it will take some time before they are phased out. Also, data from serial-based traffic is sourced mostly from SCADA; thus, when SCADA is compromised, the data inferred from it can be misleading. To the best of our knowledge, there is no work on data collection involving serial-based protocols such as DNP3 serial, Modbus-RTU and IEC 60870-5-101; hence, we propose a custom serial sniffer that captures data from serial devices and logs them. Such complementary data can be transmitted to a Security Operations Centre (SOC) to provide a second view to confirm the substation's status. Our bench-marking of the serial sniffer shows negligible utilization of CPU and memory resources for DNP3 serial, Modbus-RTU and IEC 60870-5-101; and we can conclude that it is stable across all protocols. Our sniffer code can be embedded into serial-to-Ethernet protocol converters due to its lightweight nature. Our work can complement data collection within substations until the migration to Ethernet-based protocols is complete.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".