CLDP=FATD: Secure Federated Averaging Threat Detection Framework for Intelligent Vehicle Sensor Networks Based on Client-Level Differential Privacy
Bibliographic record
Abstract
The certification of real-time information in vehicles depends on threat detection. Intelligent vehicle sensor networks (IVSNs) have revolutionized modern transportation systems, enhancing traffic management and providing greater comfort. However, the increased use of smart sensing technologies has made connected and intelligent vehicles (CIVs) an attractive target for unauthorized access. Consequently, CIV owners are keen to ensure the security of their vehicle information, particularly the positioning, timing, and navigation of their vehicles. This article proposes a federated framework that utilizes client-level differential privacy (CLDP) to prevent privacy attacks, such as model inversion and membership inference attacks. In these attacks, an unauthorized party attempts to extract sensitive data from the model’s outputs to exploit its predictive capabilities. The CLDP-federated averaging threat detection (CLDP-FATD) approach utilizes Rényi-DP-Fed-Avg (RDP)/$(\alpha, \epsilon)$-DP, as an alternative to traditional DP algorithms to safeguard privacy and prevent data leakage within the federated learning (FL) framework. The efficacy of the proposed framework was evaluated using a GPS spoofing attack dataset. The findings demonstrate that the proposed scheme ensures collaborative privacy-utility tradeoff for CIV, achieving a minimal privacy budget$(\epsilon)$of 0.99 at 94.27% and 2.0 at 88.42% for binary and multiclass, respectively, outperforming existing approaches.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".