Stealthy Data Corruption Attack Against Road Traffic Congestion Avoidance Applications
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) leverage open and real-time sharing of traffic data to enable more efficient transportation. However, the data exchanged over the vehicular network are easily corruptible via attacks known as misbehaviours. Misbehaviour detectors have been extensively developed but remain siloed and lack consideration of advanced attacks amalgamating multiple misbehaviours. These may be carried out as part of Advanced Persistent Threats. This paper presents a new approach to specifically designing stealthy data corruption attacks within ITS, and by extension in other data-reliant Cyber-Physical Systems. A Stackelberg security game is devised to model the actions of evasive attackers targeting congestion avoidance applications. The game is then solved to produce the optimal attack and defense strategies. The new stealthy attack achieves the intended long-term impact while improving evasion performance. This research direction exploring sophisticated attacks will allow to advance the design of robust misbehavior detection systems.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".