Cybersecurity and process safety synergy: An analytical exploration of cyberattack‐induced incidents
Bibliographic record
Abstract
Abstract In recent years, cyber‐connected industrial control systems (ICS) for remote operations have increased cyber and process risks. While process safety is widely studied, its connectivity with the cyber threat has not been well explored. It is crucial to study cybersecurity and process safety in an integrated way to ensure safe operations. This study addresses this gap by first analyzing the cyber incidents related to ICS since 1990. Subsequently, it connects cyber incidents with process accidents by Bowtie based on the ATT&CK framework. It further develops a Bayesian network due to the insignificant probabilities by Bowtie. The developed model is explained with case analysis. This study confirms that the process industry is prone to cyberattacks, and the field controllers are the main targets of attacks. The study observes that the safety instrument system (SIS) is critical for both the attackers and neutralizing the attacks (defenders). Moreover, attackers deploy newer approaches to attack the ICS, and therefore, methods of threat assessment and its neutralizing strategies should be dynamic.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".