Machine Learning Techniques for Adaptive Consensus Mechanism in Blockchain Systems

Blockchain technology has emerged as a critical infrastructure for the development of decentralized applications and services, offering transparency, security, and resilience. Central to its operation is the consensus mechanism, which ensures that all participants in the system agree on its state. However, classic consensus algorithms often have disadvantages in terms of performance and security when conditions and network load change, as they are not able to adapt dynamically. This paper studies machine learning techniques for developing a more flexible and adaptive consensus mechanism. Specifically, two methods are used: reinforcement learning using Deep Q-Networks, and the LinTS algorithm, which belongs to the Multi-Armed Bandits category. The goal is to select the most appropriate consensus algorithm depending on the system conditions and to maximize metrics such as transaction rate, resource consumption, stability, and security. The experimental results show that the use of machine learning models leads to improved performance compared to static consensus algorithms, highlighting the importance of adaptability in real blockchain environments. The use of machine learning in real blockchain environments shows many positive results, indicating that research in this field should continue. Finally, future directions are proposed that can further enhance the adaptability and efficiency of consensus systems.