Stealthy deception attacks for cyber-physical systems
Bibliographic record
Abstract
We study the security of Cyber-Physical Systems (CPS) in the context of the supervisory control layer. Specifically, we propose a general model of a CPS attacker in the framework of Discrete Event Systems (DES) and investigate the problem of synthesizing an attack strategy for a given controlled system. Our model captures a class of deception attacks, where the attacker has the ability to modify a subset of sensor readings and mislead the supervisor, with the goal of inducing the system into an undesirable state. We introduce a new type of a bipartite transition structure, called Insertion-Deletion Attack structure (IDA), to capture the game-like interaction between the supervisor and the environment (which includes the system and attacker). This structure is a discrete transition system that embeds information about all possible attacker's stealthy actions, and all states (some possibly unsafe) that become reachable as a result of those actions. We present a procedure for the construction of the IDA and discuss its properties. Based on the IDA, we discuss the characterization of successful stealthy attacks, i.e., attacks that avoid detection from the supervisor and cause damage to the system.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".