Unmanned Aerial Vehicles-based blockchain-inspired Intelligent framework for collaborative intrusion detection

Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains such as surveillance, logistics, and disaster management. However, ensuring the safety, security, and trustworthiness of UAV operations remains a significant challenge, primarily due to vulnerabilities in centralized data processing architectures. Traditional UAV systems rely on remote cloud servers to perform machine learning (ML)-based analytics, which introduces issues such as data exposure, latency, scalability bottlenecks, and susceptibility to cyberattacks during data transmission and storage. These challenges underscore the urgent need for a decentralized, verifiable, and privacy-preser ving learning mechanism that can support collaborative UAV intelligence without centralized control. To address these limitations, this study proposes a blockchain-enabled distributed ML framework that facilitates secure, peer-to-peer collaboration among UAV nodes. The framework integrates blockchain’s immutable ledger and smart contracts with decentralized ML models, enabling UAVs to share and validate trained models rather than raw data. This ensures data confidentiality, integrity, and transparency throughout the learning process. A stacking-based ensemble mechanism is employed to enhance predictive performance through collaborative knowledge aggregation. The proposed system is experimentally validated using a collaborative intrusion detection (ID) scenario using the KDD99 network attack data set and real-world implementation. The results demonstrate significant improvements in detection accuracy, latency and F1-score compared to conventional centralized ML methods, achieving an average accuracy of 97.9%, latency 198ms, and F1-score exceeding 97%. These outcomes confirm that the integration of blockchain and decentralized ML effectively mitigates cybersecurity risks while enabling scalable, trustworthy UAV intelligence.