Blockchain-Enabled Federated Learning for Enhanced Collaborative Intrusion Detection in Vehicular Edge Computing
Why this work is in the frame
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Bibliographic record
Abstract
Intelligent Transportation Systems (ITSs) are transforming the global monitoring of road safety. These systems, including vehicular networks and transportation infrastructure, are vulnerable to several security issues, which could disrupt services and potentially cause harm to the users. It is crucial to establish robust security measures to protect against evolving attacks and ensure the safe and reliable operation of ITS. Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) are mainly used to enhance the security of ITS. The adoption of AI-based techniques to secure ITS against new emerging threats has been limited due to a lack of realistic and recent data on these types of attacks ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i.e.,$</tex-math> </inline-formula> zero-day attacks). In this context, we introduce a novel Edge-based Framework that uses Federated Learning (FL) and blockchain to secure ITS against new emerging threats. In particular, our proposed framework consists of (1) a novel distributed Edge-based architecture that allows multiple Edge nodes to securely collaborate while preserving their privacy; and (2) a decentralized and secure reputation system based on blockchain technology to maintain the reliability and trustworthiness of the FL process within the ITS; This system manages reputation data for individual nodes (such as vehicles), guaranteeing the integrity of the FL training process. Experiment results using the UNSW-NB15 dataset show that our proposed framework achieves high accuracy and F1 score (99%) in detecting new threats while ensuring the privacy and reliability of the whole ITS. These results demonstrate the effectiveness of our proposed framework in securing ITS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it