ArticleScientific reports2025
Quantum resistant blockchain and deep learning revolutionize secure communications for autonomous vehicles.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The blistering appearance of autonomous vehicle networks (AVNs) represents extreme cybersecurity dangers, particularly with the forthcoming danger of quantum computing, which is capable of overcoming traditional defenses on the basis of cryptography. This paper offers a new architecture that integrates quantum-resistant blockchain technology and deep learning to offer excellent cybersecurity in intelligent transportation systems (ITS). Three contributions are proposed: (1) a postquantum safe communication scheme, which will secure vehicle-to-anything (V2X) communications; (2) a decentralized blockchain-based data-sharing system, which will be utilized to ensure the integrity, privacy, and integrity of vehicular data; and (3) the application of a deep learning model as an anomaly detector and real-time threat mitigation to vehicle-to-anything (V2X) communications. The experimental tests demonstrate that the deep learning architectures for detecting cyber threats are 95.2% accurate, 94.1% precise, and 93.5% recall, with blockchain data integrity of 99.9% throughput of 150 transactions per second and a block validation time of 1.5 s. In context, the framework will help improve ITSs by offering safer roads, efficient communication infrastructure, and efficient data exchange, which will establish the road to successful and safe application of autonomous transportation systems in the real world.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.