Risk-Based Methodology for Optimal Cryptoperiod Calculation in ICSs Under Data Siphoning Attack
Bibliographic record
Abstract
Siphoning (or stealing) of in-transit operational data from industrial-control or critical-infrastructure systems is a highly concerning form of intrusion that can be done not only for the purposes of cyber espionage, but is often a key precursor for far more consequential cyber-physical attacks. Data encryption is viewed as one of the key defences against in-transit data siphoning, as it makes the stolen/exfiltrated data incomprehensible to the adversary. Though, this is obviously true only for as long as the key used to encrypt the exfiltrated data is not accessible to the adversary. In this paper we briefly discuss various strategies an ICS adversary can deploy to acquire encryption key(s) and thus ensure the ultimate success of their in-transit data siphoning attack. From the defender's perspective, we also discuss the importance of periodic encryption key rotation, as well as the importance of determining the most optimal cryptoperiod(s) - i.e., the actual time interval after which one encryption key should be replaced with another. While very short cryptoperiods may seem like the best choice from the security standpoint, practically this strategy may result in serious performance degradation, or in some cases may be completely infeasible. Unfortunately, the specifics on how to actually determine/calculate the optimal length of cryptoperiods in IT and ICS systems are not only absent from the relevant industry standards, but are also largely overlooked in more general research literature. As the main contributions of this paper, we present a detailed attack-tree model of data siphoning in ICSs, and we introduce our novel risk-based methodology for calculation of optimal cryptoperiods in real-world ICSs. According to our knowledge, this is the first research study attempting to address as well as solve the problem of optimal cryptoperiod calculation, not only in the context of ICSs but also wider IT 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".