DDoS Attack Prevention in Autonomous Vehicle’s OTA Updates: Combining PBFT Consensus and Distributed Firewall in Hyperledger Fabric Blockchain
Bibliographic record
Abstract
The advent of connected autonomous vehicles (CAVs) is bringing forth a revolutionary new era of technology transforming transportation. For traffic to be optimized and safe, efficient vehicle-to-everything collaboration and improved autonomous vehicles (AV) decision-making are crucial. It becomes essential to make decisions in real time using information from vehicle sensors, software, and traffic data. As a part of such an In-Vehicle Network (IVN), over-the-air (OTA) software update service in CAVs needs to be facilitated rapidly, reliably, and securely. However, by taking advantage of vulnerabilities, attackers may quickly target the OTA software update as part of botnets to execute distributed denial-of-service (DDoS) attacks. The enormous volume and widespread nature of these DDoS cyber-attacks make it vital for the CAV industry to work quickly on identifying and preventing these threats. This paper proposes proof-of-concept experiments with the Hyperledger Fabric (HLF) Blockchain model to detect and prevent DDoS attacks in CAVs, OTA update systems. The proposed method implements Practical Byzantine Fault Tolerance (PBFT) as the consensus mechanism and a distributed firewall to ensure the ledger is secure and tamper’ proof. The system is tested on the Amazon Elastic Compute Cloud (EC2) Blockchain (BC) platform. The results show that the proposed approach effectively prevents DDoS attacks while ensuring fast transaction execution time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".