Enhancing Suricata intrusion detection system for cyber security in SCADA networks
Bibliographic record
Abstract
Industrial Control and SCADA (Supervisory Control and Data Acquisition) networks control critical infrastructure such as power plants, nuclear facilities, and water supply systems. These systems are increasingly the target of cyber attacks by threat actors of different kinds, with successful attacks having the potential to cause damage, cost and injury/loss of life. As a result, there is a strong need for enhanced tools to detect cyber threats in SCADA networks. This paper makes a number of contributions to advance research in this area. First, we study the level of support for SCADA protocols in well-known open source intrusion detection systems (IDS). Second, we select a specific IDS, Suricata, and enhance it to include support for detecting threats against SCADA systems running the EtherNet/IP (ENIP) industrial control protocol. Finally, we conduct a traffic-based study to evaluate the performance of the new ENIP module in Suricata - analyzing its performance in low performance hardware systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".