Guide to Developing Case-based Attack Scenarios and Establishing Defense Strategies for Cybersecurity Exercise in ICS Environment
Bibliographic record
Abstract
Abstract Critical infrastructure mostly performs its role through an industrial control system (ICS). Organizations that operate security-related facilities often conduct adversarial simulation exercises between the so-called red team, which carries out attacks, and the blue team, which is responsible for defense. For the exercise to be effective, adversarial activities should include clearly delineated attack scenarios and corresponding defensive activities. Although government agencies and organizations in each country recognize the importance of exercises and propose various guidelines and practices, there still needs to be systematic guides for deriving cyberattack scenarios or defense strategies. This paper proposes a guide for establishing realistic attack scenarios and defense strategies for cybersecurity exercises in ICS environments. The proposed guide is largely divided into attack scenario generation and defensive strategy derivation. Attack scenario generation is further divided into four steps: generating attack references, deriving attack sequence, mapping threat information, and mapping vulnerable implementation patterns. Deriving a defensive strategy consists of two steps parallel to developing an attack scenario: deriving containment and eradication. Through a case study, we showed that a clear exercise plan could be established from the proposed guide. Additionally, we discuss some possible uses and limitations of our proposal.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.026 |
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".