基于极限学习机的网络安全态势预测模型

Network Security Situation Forecasting Model Based on Extreme Learning Machine

态势预测是保证网络安全的前提条件,针对网络安全态势复杂、非线性的变化特点,提出了基于极限学习机的网络安全态势预测模型。收集网络安全态势的历史数据,并通过数据预处理便于后继建模,然后采用极限学习机对网络安全态势进行训练,建立网络安全态势的预测模型,最后通过仿真对比实验验证其有效性。结果表明,极限学习机可以刻画网络安全的将来变化态势,获得了高精度的网络安全态势预测结果,同时网络安全态势的建模效率得到了明显的提高。

Situation prediction is a prerequisite to ensure network security. According to the complex and nonlinear characteris- tics of network security situation, a network security situation prediction model based on extreme learning machine is proposed. The first is to collect historical data of network security situation, and to facilitate the subsequent modeling through the pretreatment, and then it uses extreme learning machine to train the network security situation prediction model, establishes a net- work security situation, finally the simulation experiments verify its effectiveness. The results show that the extreme learning can depict the future change trend of network security, and obtain a high precision prediction result of network security situa- tion, at the same time; the modeling efficiency of network security situation has been obviously improved.