Boosting Bitcoin Security: Innovative Threat Detection with Ensemble Learning and Water Cycle Algorithm Optimization

Abstract

With the growing significance of blockchain-based Bitcoin systems, ensuring robust security measures is imperative. This research introduces an innovative approach to enhance system-level threat detection through the integration of a novel ensemble learning model, bolstered by the Water Cycle Algorithm (WCA). The proposed model aims to address the evolving landscape of security challenges in the blockchain domain, specifically targeting the detection of threats that may compromise the integrity and efficiency of Bitcoin systems.The ensemble learning model combines diverse algorithms, leveraging their collective intelligence to improve accuracy and resilience against sophisticated threats. The integration of the Water Cycle Algorithm further enhances the adaptability of the model by mimicking the natural processes of water cycles for dynamic optimization. This adaptive feature enables the system to efficiently respond to emerging threats, ensuring real-time threat detection and mitigation.