Detection model for network access data tampering attacks with blockchain technology

Abstract

This paper studies the detection model of network access data tampering attack based on blockchain technology to solve the problem of over-dependence on central server and easy data tampering in traditional network environment. The model uses decentralization and encryption technology to monitor user behavior in real time through smart contracts, enhances data protection with SHA-256 hash algorithm, and combines consensus algorithm to ensure data consistency and security. The experimental results show that the model performs well in detecting multiple attack types with an accuracy of 99.51% and an F1 score of 0.98, far exceeding traditional methods and other deep learning techniques. The model shows good robustness under multi-node attacks, even with 200 attack nodes, the recognition accuracy is still close to 90%, and the response time is less than 3 seconds. Cross-platform testing showed that the model quickly and consistently detected tampering on both Ethereum and Hyperledger, with an average detection time between 0.33 and 0.47 seconds.The hardware acceleration test further shows that the processing speed and hardware utilization of TPU and GPU have been improved, with TPU processing speed reaching 135 MB/s and GPU 122 MB/s. This study will provide a theoretical basis for improving the security, effectiveness and reliability of current network systems, and also lay a solid theoretical and technical foundation for network applications in future network environments.