Automated Risk‐Based Cryptoperiod Calculation in <scp>ICSs</scp>: Analytical Framework and Software Tool
Bibliographic record
Abstract
ABSTRACT Internal network and system reconnaissance is one of the first crucial stages of most cyber attacks, though it plays an especially important role in attacks on complex industrial control systems (ICSs) that span both IT and OT environments. Frequently, besides device enumeration and scanning, a malicious ICS reconnaissance campaign will also involve siphoning of important operational in‐transit data, which can provide the adversary with invaluable insights and information about the functioning of the target system. In this work, by specifically focusing on industrial systems that deploy the OPC UA standard, we first give a brief overview of different data siphoning strategies possibly conducted by an adversary. We then discuss the important role of periodic encryption‐key rotation (i.e., limiting of cryptoperiod length/duration) to minimize the ultimate risk and impact of data siphoning. We also point to the lack of a clear guideline in industry standards and research literature on how cryptoperiod(s) assigned to an OPC UA security group should be determined/calculated. We then introduce our novel framework and tool for Automatic Risk‐based Cryptoperiod Calculation (ARC‐C) intended to optimize the overall system performance. We demonstrate the use and usefulness of this tool by applying it to a hypothetical but highly plausible Water Treatment Plant environment built on real‐world models.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".