基于时间序列输入结合SIQNN的网络安全态势感知研究

Research on Network Security Situation Based on Sequence Input Combined with Quantum Neural Network

针对传统网络安全态势感知精度低的问题,提出一种时间序列输入结合量子神经网络的网络安全态势预测模型,并设计自上而下的基于SIQNN模型的网络安全态势感知方案。结果表明,所提方案相较于BPNN模型和PNN模型,具有更好的预测能力,平均识别准确率达95.98%,平均预测准确率达82.02%,具有明显的识别和预测优势,可用于网络安全态势感知。

Aiming at the problem of low precision of traditional network security situation awareness,a network security situation prediction model based on time series input and quantum neural network is proposed,and a top-down network security situation awareness scheme based on SIQNN model is designed.The results show that the proposed scheme has better prediction ability than BPNN and PNN,the average recognition accuracy is 95.98%,and the average prediction accuracy is 82.02%.It can be used in network security situational awareness.