BSMACRN: Design of an Efficient Blockchain-Based Security Model for Improving Attack-Resilience of Cognitive Radio Ad-hoc Networks

Abstract

Cognitive Radio Ad-hoc Networks (CRAHNs) are under constant attacks from compromised primary & secondary nodes. These attacks focus on bandwidth manipulation, internal configuration manipulation, and selective spoofing, which can disturb the normal working of the CRAHNs. Researchers propose various security models to mitigate these attacks, each with limitations. Most of these models have higher complexity, while others cannot be used to mitigate multiple attack types. To overcome these issues while maintaining higher security and Quality of Service (QoS) under attacks, this text proposes a design of a novel blockchain-based security model for improving attack resilience in CRAHNs. The model initially collects multiple information sets from different cognitive radio controllers and creates active & redundant miners for the storage of these sets. The number of active & redundant miners is decided via a Mayfly Optimizer (MO) Model, which assists in improving resource utilization while reducing deployment costs. Cognitive rules and configurations are stored on these nodes and updated via a secure blockchain verification. Due to this, the proposed model demonstrated significant improvements in cognitive radio communications across various metrics, even under different attack scenarios. It reduced communication delay by up to 18.5%, increased communication throughput by up to 19.5%, and improved the Packet Delivery Ratio (PDR) by up to 19.4% when compared with existing models such as SRC [2], Prob Less [4], and DDQL [13]. Additionally, the model achieved energy savings of up to 12.5%. These enhancements were made possible by the optimized selection of miner nodes, enabling quicker mining for high-speed communication, low-energy mining tasks for prolonged use, and high-performance mining for consistency. The results affirm the model's suitability for various real-time cognitive radio scenarios. Due to the integration of the MO Model, the CRAHN showcases better communication speed, lower energy consumption, higher throughput, and higher packet delivery performance when compared with existing methods under real-time scenarios.