Secure Multicast Routing Against Collaborative Attacks in FANETs With CF-mMIMO and STAR-RIS: Blockchain and Federated Learning Design

• Published in: IEEE internet of things journal Vol. 12; no. 12; pp. 22404 - 22426
• Main Authors: Pramitarini, Yushintia, Hendra Yoga Perdana, Ridho, Shim, Kyusung, An, Beongku
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.06.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ad hoc networks; Blockchain; Blockchains; cell-free massive MIMO (CF-mMIMO); Clustering; Collaboration; collaborative attack; Cybersecurity; Data models; Design optimization; Federated learning; federated learning (FL); Flight; flying ad hoc networks (FANETs); Incident waves; Internet of Things; Multicasting; Network security; Nodes; Protocol; Protocols; Quality of service; Quality of service architectures; Real time; Real-time systems; Reconfigurable intelligent surfaces; Routing; Routing protocols; secure multicast routing; Signal quality; simultaneously transmitting and reflecting-reconfigurable intelligent surfaces (STAR-RIS)
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3551746

In this article, we propose novel federated learning (FL) and blockchain-based secure multicast routing (FBSMR) protocol in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO) and simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS) effectively avoiding collaborative attacks. The proposed FBSMR protocol integrates FL with blockchain to enhance security and prevent collaborative attacks during the routing process. Besides, by utilizing a cross-layer design, the proposed FBSMR can enhance network security and Quality-of-Service (QoS) performance. Specifically, we implement a blockchain-based approach to support secure multicast routing, which efficiently detects and isolates malicious nodes. By using these techniques, all participating nodes achieve consensus on the validity of routing paths, thereby significantly enhancing overall network security. Besides, we address the cost-minimization problem in the proposed cross-layer design by optimizing the weight values of physical layer information, data link layer information, and network layer information subject to the minimum sequence numbers, maximum end-to-end delay, and hop count constraints. To further enhance the coverage area, improve receive signal quality, and reduce the number of hops, we leverage the capabilities of STAR-RIS technology attached to the AAV (F-STAR-RIS) to refract and reflect incident waves toward desired positions, enabling significant improvements in signal quality and transmission coverage. Additionally, the FL framework is employed for real-time prediction of the secure next node, utilizing local data from each flying access point (F-AP) to predict the optimal next node, STAR-RIS configuration, and phase shift at the STAR-RIS. Simulation results demonstrate that the proposed FBSMR protocol, combined with the FedChain-based clustering protocol, establishes a more secure route against collaborative attacks and outperforms benchmark protocols in terms of connectivity, stability, and security performance.