In the mind of an insider attacker on cyber‐physical systems and how not being fooled
Bibliographic record
Abstract
Insider attacks are one of the most serious threats for cyber‐physical systems, they have potentials to inflict destructive damages on physical processes while remaining stealthy. This study dissects several insider attacks by examining their modes of data tampering. To set the scene, a general framework of a cyber‐physical system is constructed, a pattern characterising insider attacks is introduced in the form of attack goals, resources, constraints, modes, and attack paths. The conditions under which the attackers can maintain stealthy are examined in both temporal and spatial domains. With the inside knowledge, an attacker can use an attack graph to exploit system vulnerabilities and determine the high impact targets. To demonstrate the effectiveness of this analysis, a cyber‐physical system is constructed by using networks and a nuclear process control test facility with ports deliberately left open for attackers. Two attack scenarios are staged, and their characteristics and impacts are examined. This case study demonstrates how an insider attacker might mount an attack by using data tampering and how they can maintain stealthy before major damages are done to the physical system. The significance of this study is to uncover the techniques of insider attackers so that vulnerabilities can be mended.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".