基于灰关联熵的网络安全态势kalman预测算法

在评估当前网络安全态势的基础上,掌握未来一段时间的网络安全态势,能够为网络管理者做出安全防护的决策提供有效的信息。利用网络安全态势值具有非线性时间序列的特点,提出一种基于灰关联熵的网络安全态势卡尔曼预测算法。首先应用灰关联熵分析方法对网络安全态势的各种影响因素做关联度分析,由此选出关键影响因素,接着根据这些影响因素建立相应的过程方程和预测方程。最后应用卡尔曼滤波递推地进行网络安全态势预测。实验结果表明该算法的预测精度优于传统的GM(1,1)算法和普通卡尔曼算法,算法适应性和实时性优于RBF算法。

Kalman algorithm of Network Security Situation Prediction based on Grey relation entropy

Based on the assessment of current network security situation, we studied the problem of network security situation prediction by using the algorithm of Kalman, which will help the network managers to make security decisions and provide them effective information. The paper presents a prediction algorithm by applying the feature of nonlinear and time series based on Grey relation entropy, which can analyze entropy relation grade of factors influencing the value of network security situation. Thus, we can select key factors and create the appropriate process equation and prediction equation based on these factors, and can recursively predict network security situation base on Kalman filtering. Experiment results show that the prediction with this method is more precise than GM(1,1) and kalman algorithm, its adaptability and performance of real-time is better than RBF algorithm.