Network security situation prediction based on hyper parameter optimization of relevance vector machine

doi:10.11772/j.issn.1001-9081.2015.07.1888

Abstract

Abstract:

To deal with the existing problems of current network security situation prediction methods, such as overfitting, underfitting, various free variables and insufficient prediction accuracy, this paper proposed a RVM (Relevance Vector Machine) model with an improved Simulated Annealing (PSA-RVM) to solve the network security situation prediction problems. In the process of prediction, the sample data of network security situation were firstly reconstructed in phase-space to form the training sample set; then, Powell algorithm was used to improve Simulated Annealing (PSA) and RVM was inserted into the target function calculation process of PSA algorithm to optimize RVM hyper parameters and to acquire a network security situation prediction model with enhanced learning capability and prediction accuracy. The simulation experiment results indicate that the proposed method has higher prediction accuracy, with Mean Average Percentage Error (MAPE) and Root Mean Squared Error (RMSE) of 0.39256 and 0.01261, higher than Elman and Particle Swarm Optimization-based Support Vector Regression (PSO-SVR) models; the proposed method can depict well the changing tendency of network security situation, which is helpful for network administrators to control the development trend of future network security situation and take the initiative to take network defense measures.

Key words: network security situation, Relevance Vector Machine (RVM), Powell algorithm, Simulated Annealing (SA), prediction, hyper parameter

摘要：

针对当前网络安全态势预测方法存在的过学习与欠学习、自由参数多、预测精度不高等问题,提出使用一种改进模拟退火法优化的相关向量机模型(PSA-RVM)来解决网络安全态势预测问题。在预测过程中,首先对网络安全态势样本数据进行相空间重构形成训练样本集;然后,利用Powell算法改进模拟退火(PSA)法,并将相关向量机(RVM)嵌入到PSA算法的目标函数计算过程中,优化RVM超参数,以得到学习能力、预测精度提升的网络安全态势预测模型。仿真实例表明,所提方法具有较高的预测精度,平均相对误差(MAPE)和均方根误差(RMSE)分别为0.39256和0.01261,均优于Elman和PSO-SVR模型;所提方法能够较好地刻画网络安全态势的变化趋势,有助于网络管理人员把握未来网络安全态势发展趋势,从而提前主动采取相应的网络防御措施。

CLC Number:

TP393.08

XIAO Hanjie, SANG Xiuli. Network security situation prediction based on hyper parameter optimization of relevance vector machine[J]. Journal of Computer Applications, 2015, 35(7): 1888-1891.

肖汉杰, 桑秀丽. 相关向量机超参数优化的网络安全态势预测[J]. 计算机应用, 2015, 35(7): 1888-1891.

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