Block-chain Encryption Search Algorithm for Educational System Using Improved Neural Network

The interconnection of the educational system to the blockchain means the challenge of security, reliability, credibility, and confidentiality of information and data. Information search and data access of blockchain are realized by accessing PHR from a database and vulnerable to a variety of security vulnerabilities, even though blockchains provide a secure search mechanism However, network physical attack, deception injection attack, electromagnetic crosstalk attack, malicious code injection, and other vulnerability attacks always bring high risk to the information and data security of education system. Based on the model of security threat and intrusion detection, we present a security search and access model to information and data of blockchain with an improved BP neural network. By introducing a dynamic self-adjusting coefficient, the search and access efficiency, real-time performance, and throughput are improved. In this model, homomorphic encryption technology is used to secure search and database access based on keywords. A key revocation and regeneration strategy is proposed to integrate blockchain and the security chain. Compared with the existing models of search and access based on BP and Fuzzy networks, the method proposed in this paper provides the premise of data sharing, effective data access, and better information search security for education system block chain information flow.