基于机器学习的网络安全态势感知系统研究

Research on network security awareness system based on machine learning

随着信息技术的快速发展,网络环境变得更为复杂,网络攻击手段也越来越多,网络空间的安全性就更为重要。在此背景下,网络安全态势感知技术应运而生,成为评估网络安全现状,洞察网络安全风险,预测网络未来发展的关键技术。研究网络安全态势感知系统可以提高网络的监控能力、应急响应能力等,文章分析了当前网络安全态势感知模型和网络安全态势指标,采用双向LSTM网络安全预测模型,并用贝叶斯优化方法确定模型的超参数,从而提高了网络安全态势预测模型精度与效率。

With the rapid development of information technology,the network environment has become more complex,and there are more and more methods of network attacks,making the security of the cyberspace even more important.In this context,network security situational awareness technology has emerged as a key technology for assessing the current state of network security,insight into network security risks,and predicting the future development of the network.The research on network security situation awareness system can improve the network monitoring ability,emergency response ability,etc.This paper analyzes the current network security situation awareness model and network security situation indicators,uses two-way LSTM network security prediction model,and uses Bayesian optimization method to determine the model’s hyperparameter,thus improving the accuracy and efficiency of network security situation prediction model.