Network Security Situation Prediction Approach Based on Clonal Selection and SCGM(1,1)c Model
Network Security Situation Prediction Approach Based on Clonal Selection and SCGM(1,1)c Model
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DOI：
10.6138/jit.2016.17.3.20130405
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发表时间：
2016-05
影响因子：
1.6
通讯作者：
Yuanquan Shi;Renfa Li;Xiaoning Peng;Guangxue Yue
中科院分区：
文献类型：
--
作者：
Yuanquan Shi;Renfa Li;Xiaoning Peng;Guangxue Yue
由于网络安全状况受网络攻击的威胁程度、网络服务的重要性和网络系统的脆弱性等因素的影响，其状况评价值具有模糊性。针对态势评估值的不确定性和网络安全态势的实时性需求，提出了一种基于克隆选择和系统云SCGM(1,1)c模型的改进预测模型，即CS-SCGM(1,1)c模型，用于网络安全态势的时间序列预测。在CS-SCGM(1,1)c模型中，将SCGM(1,1)c模型作为基本预测模型，利用克隆选择原理对CS-SCGM(1,1)c模型的acs和bcs参数进行优化，以提高模型的预测精度。实验结果表明，CS-SCGM(1,1)c模型比SCGM(1,1)c模型更准确，为网络安全状况预测提供了一种有效的方法。
Due to network security situation affected by the threat degree of network attacks, the significance of network services and the frangibility of network system, its situation evaluation values possess fuzzification. For the uncertainty of situation evaluation values and the real-timely demand of network security situation, an improved prediction model based on clonal selection and system cloud SCGM(1,1)c model, namely CS-SCGM(1,1)c model, is proposed to be used for predicting time series of network security situation. In CS-SCGM(1,1)c model, SCGM(1,1)c model is viewed as the basic prediction model, and clonal selection principle is used for optimizing the parameters acs and bcs of CS-SCGM(1,1)c model in order to improving the prediction precision of the proposed model. The experimental results show that CS-SCGM(1,1)c model is more accurate than SCGM(1,1)c model, and provide an effective prediction approach for network security situation.