Quantum-Resilient Blockchain-Integrated Covert Authentication for UAV Networks with AI-Driven Consensus Mechanism
Bibliographic record
Abstract
The rapid expansion of Unmanned Aerial Vehicle (UAV) networks necessitates robust security frameworks to mitigate vulnerabilities such as identity theft, information leakage, and brute-force attacks. Traditional authentication mechanisms often rely on public channel identity verification, exposing them to security risks. Blockchain technology provides a decentralized, tamper-proof solution, yet existing consensus mechanisms suffer from scalability issues in dynamic UAV environments. This paper proposes a Quantum-Resilient Blockchain-Integrated Covert Authentication (Q-BICA) mechanism, which enhances UAV security by leveraging covert communication and an AI-driven quantumresistant consensus protocol. Identity information is transmitted via covert tags embedded in standard modulated signals, significantly reducing susceptibility to eavesdropping and brute-force decryption. Furthermore, an AI-Optimized Quantum-Resilient Practical Byzantine Fault Tolerance (AI-QRPBFT) consensus algorithm is introduced, where machine learning (ML) dynamically adjusts node weights based on UAV behavior, historical authentication outcomes, and real-time channel conditions, while employing lattice-based cryptographic techniques to resist quantum computing attacks. This approach enhances fault tolerance, scalability, and future-proof security in large-scale UAV networks. Simulation results demonstrate that Q-BICA outperforms existing authentication models in terms of security robustness, network scalability, and resilience under low Signal-to-Noise Ratio (SNR) conditions. The proposed mechanism represents a novel integration of covert authentication with an AI-driven quantum-resilient blockchain consensus, ensuring secure and
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".