Security Threat Early Warning of Distance Education System Based on Blockchain

【Author】 Chen, Zhihua; Srivastava, Gautam

【Source】JOURNAL OF INTERNET TECHNOLOGY

【影响因子】1.140

【Abstract】To ensure the safe and stable operation of distance education systems, a security threat early warning technology based on blockchain is proposed for distance education system, which builds a security threat warning model. It uses the data interface in the interface layer to connect the teacher and student client. Then, the network behavior data of the distance education system is collected and transmitted to the data layer, where data blocks exchange the behavior data of the distance education system, and then the chain structured behavior data is generated and transmitted to the consensus layer. After the behavior data is transmitted to the incentive layer through the consensus layer, the distribution mechanism and basis are used to process and transfer the behavior data to the contract layer. The contract layer uses the threat early warning model to calculate the behavior data, and then conducts threat rating and early warning response on the data. It transmits the threat rating and early warning results to the application layer and presents them to users, thus realizing the security threat early warning of the distance education system. The experimental results show that the transcoding rate of this technology for the network behavior data of the distance education system is higher than 0.97, the early warning accuracy for the 10 types of network data of the distance education system can reach 100%, and the credibility of the early warning security threat of the types of DDOS IP, DDOS IP, phishing website URL address, and mobile malicious server IP address is higher than 0.96. Therefore, the technology has a strong capacity of behavior data storage in distance education systems, and can effectively warn different types of security threat in distance education systems. It has a more excellent application effect.

【Keywords】threat information; Early warning technology; Information; early warning

【发表时间】2023 SEP

【收录时间】2023-11-08

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-安全监控