Authentication is of paramount significance to cybersecurity. However, most of conventional authentication schemes are implemented in a centralized mode, in which potential problems that could arise include single-point failure, the exposure of personal information, and the risk of identity theft. Additionally, static single-factor authentication schemes are unsuitable for dynamic environments like mobile applications. In order to tackle these difficulties, we propose a blockchain-based authentication scheme with an adaptive multi-factor authentication strategy. Our scheme features a blockchain-based authentication framework that prevents unauthorized information alteration and system corruption. Additionally, we design an adaptive multi-factor authentication strategy model to ensure trustworthy multi-factor authentication in dynamic scenarios. Last, we construct a Raft-based consensus model to select an authoritative leading node for rapid authentication. The security analysis demonstrates the effectiveness of the proposed scheme in effectively countering various forms of cyberattacks targeted at authentication systems, and experiments demonstrate its superior effectiveness and efficiency compared to existing studies.

Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. This allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efficiency and flexibility. Improving professional guidelines for social media interactions is crucial to address the wide range of complex issues that arise in today’s digital age. It is imperative to enhance and update professional guidelines regarding social media interactions in order to effectively tackle the multitude of intricate issues that emerge. In this paper, we propose a reinforcement learning (RL) method for handling dynamic resource allocation (DRA) and load balancing (LB) activity in a cloud environment and achieve good scalability and a significant improvement in performance. To address this matter, we propose a dynamic load balancing technique based on Q-learning, a reinforcement learning algorithm. Our technique leverages Q-learning to acquire an optimal policy for resource allocation in real-time based on existing workload, resource accessibility, and user preferences. We introduce a reward function that takes into account performance metrics such as response time and resource consumption, as well as cost considerations. We evaluate our technique through simulations and show that it outperforms traditional load balancing techniques in expressions of response time and resource utilization while also reducing overall costs. The proposed model has been compared with previous work, and the consequences show the significance of the proposed work. Our model secures a 20% improvement in scalability services. The DCL algorithm offers significant advantages over genetic and min-max algorithms in terms of training time and effectiveness. Through simulations and analysis on various datasets from the machine learning dataset repository, it has been observed that the proposed DCL algorithm outperforms both genetic and min-max algorithms. The training time can be reduced by 10% to 45%, while effectiveness is enhanced by 30% to 55%. These improvements make the DCL algorithm a promising option for enhancing training time and effectiveness in machine learning applications. Further research can be conducted to investigate the potential of combining the DCL algorithm with a supervised training algorithm, which could potentially further improve its performance and apply in real-world application.