Evaluation of Industrial Firewall Performance Issues in Automation and Control Networks
Bibliographic record
Abstract
The IEC 62443 security standards introduce the concepts of zones, conduits, and security levels as a way of segmenting and isolating the sub-systems of an industrial control network. Network segmentation logically partition the control network into separate communication zones to restrict unnecessary flow of traffic between zones of different trust level. Firewalls with deep packet inspection capabilities for filtering industrial control protocols are indispensable elements in implementing important security principles, standards, and best practices of IEC 62443. While partitioning of the industrial control network and placement of multiple firewalls at various locations provides defense in-depth against cyber-attacks, it is important to consider the impact of these firewalls on nodes distributing time critical communications. This paper attempts to (i) study network performance impact introduced by the implementation of multiple firewalls in Modbus TCP/IP industrial control networks following IEC 62443 security standards and (ii) evaluate if time constraint requirements for communications are achievable. The results reveal that the latency and jitters introduced by multilayered firewalls makes it challenging to achieve real-time communications in some industrial applications when strict IEC 62443 security standards are followed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".