A Mathematical Modeling of Stuxnet-Style Autonomous Vehicle Malware
Bibliographic record
Abstract
Autonomous vehicles (AVs) have the potential to provide new paradigms to enhance the safety, mobility, and environmental sustainability of surface transportation. However, as vehicles become more computerized and internally interconnected by electronic control systems, their vulnerability to cyber-attacks is a fast-growing concern and a national priority. Evidence from the Internet virus suggests that AVs will have critical challenges posed by epidemic-style malware like Stuxnet. This self-propagating malware is a fast and powerful way of disrupting the AV system and transportation infrastructure. This study presents a mathematical model for Stuxnet-style malware’s temporal and spatial spread. Taking cues from the field of epidemiology and ecology, the malware will be described as an infectious epidemic to capture the dynamics of temporal and spatial propagation behavior. This study is the first attempt to analyze the spread of Stuxnet-style malware on AVs. The future uses of such a model for the temporal-geographic spread of AVs-based infectious malware are discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".