摘要
为了提升光通信系统安全风险评价效果,保证光通信系统安全,设计了基于区块链技术的光通信系统安全风险评价方法。首先分析光通信系统安全风险的研究进展,设计光通信系统安全风险评价总体思路,然后设计光通信系统安全风险评价指标,运用区块链技术存储指标数据,并采用熵值法确定光通信系统安全风险评价指标的权重,最后采用RBF神经网络设计光通信系统安全风险评价模型,并与其他光通信系统安全风险评价方法进行了仿真测试。结果表明,本方法可以改善光通信系统安全风险评价效果,光通信系统安全风险评价正确率超过93.38%,评价误差控制在光通信系统安全性的合理区间内,光通信系统安全风险评价更加可信。
In order to improve the effect of security risk assessment of optical communication system and ensure the security of optical communication system, a security risk assessment method of optical communication system based on blockchain technology is designed. Firstly, the research progress of optical communication system security risk is analyzed, and the overall idea of optical communication system security risk evaluation is designed. Then, the optical communication system security risk evaluation index is designed, the index data is stored by blockchain technology, and the entropy method is used to determine the weight of optical communication system security risk evaluation index, Finally, the RBF neural network is used to design the security risk evaluation model of optical communication system, and the simulation test is carried out with other security risk evaluation methods of optical communication system. The results show that this method can improve the effect of optical communication system security risk assessment. The accuracy of optical communication system security risk assessment is more than 93.38%. The evaluation error control ensures that the optical communication system security risk assessment is more reliable within a reasonable range.
作者
王彦华
赵廷磊
王顺晔
任建强
WANG Yanhua;ZHAO Tinglei;WANG Shunye;REN Jianqiang(College of Electrical and Information Engineering,Langfang Normal University,Langfang Hebei 065000,China;College of Commuter Science and Engineering,Northeastern University,Shenyang 110819,China)
出处
《激光杂志》
CAS
北大核心
2022年第10期197-201,共5页
Laser Journal
基金
河北省科技计划项目软科学研究专项(No.20550304D)。
关键词
区块链技术
光通信系统
防篡改特性
安全风险
评价正确率
指标权重
blockchain technology
optical communication system
tamper proof characteristics
safety risk
evaluation accuracy
index weight