Enhancing Security and Privacy in Delay‐Tolerant Networks Through the Use of Blockchain Technology

ABSTRACT Delay‐tolerant networks (DTNs) are increasingly used in environments characterized by intermittent connectivity, long delays, and limited resources, such as disaster recovery, vehicular networks, and remote sensing. However, ensuring strong security and privacy in DTNs remains challenging due to unreliable communication links and the lack of continuous trust relationships among nodes. Although blockchain technology offers decentralization, immutability, and enhanced trust, its direct integration into DTNs is hindered by scalability issues, consensus latency, and computational overhead. The motivation of this study is to design a secure and privacy‐preserving DTN framework that effectively balances blockchain‐induced overhead with network performance. To address these challenges, this work proposes a blockchain‐enhanced DTN architecture incorporating multiple optimization and security mechanisms. Adaptive Bilateral Kernel Filtering (ABKF) is employed to eliminate noise while preserving critical data features. Network Function Virtualization Orchestration with Transport Layer Security (NFVO‐TLS) ensures authenticated and secure data transmission. Secure Multi‐Party Computation (SMPC) enables collaborative computation without revealing private data, while the Hypergraph Partitioning Algorithm (HGPA) improves scalable and efficient data distribution. Furthermore, a Multiplex Adaptive Modality Fusion Graph Attention Network (MAMF‐GAN) is integrated to optimize latency and throughput through intelligent data prediction and routing. Simulation results demonstrate that the proposed framework achieves a Packet Delivery Ratio (PDR) of 96%, privacy preservation of 99%, and approximately 95% resistance to impersonation and data manipulation attacks, outperforming existing DTN models. The framework is implemented using Python‐based simulations. Future work will focus on real‐time threat detection using advanced machine learning models, energy‐aware consensus optimization, and validation through real‐world DTN testbeds to enhance scalability and practical deployment.