一种基于GSA-SVM网络安全态势预测模型

A Network Security Situation Prediction Method Based on GSA-SVM

针对支持向量机的参数选择问题,结合引力搜索算法(GSA)需要设置的参数少以及全局优化能力强的特点,提出了一种GSA优化SVM参数的网络安全态势预测模型(GSA-SVM)。首先把SVM的参数视作在空间中的物体,并将SVM在该参数下预测产生的预测值和实际值之间的均方误差mse作为目标优化函数,然后GSA通过模拟万有引力规律影响下物体的运动规律不断变化参数,最终找到SVM最优参数。最后根据最优参数建立网络安全态势预测模型。在Matlab平台采用MIT Lincoln实验室提供的DARPA1999数据集进行仿真测试,仿真结果表明:相对于其它预测算法,GSA-SVM提高了网络安全态势预测的准确度,加快了网络安全态势预测的速度,为网络安全态势预测提供了一种新的解决途径。

In order to more accurately master the laws of network security situation regular and prevent some network security threats, in view of the problem of parameter selection of Support Vector Machines, plus, the gravitational search algorithm （GSA） is characterized by few parameters needed and having great ability in global optimization, a network security situation prediction model （GSA-SVM） is proposed for GSA optimization SVM parameters. First, the parameters of SVM are treated as objects in space, and mean square error （MSE） of predicted value and actual value of SVM under this parameter is used as the objective optimization function, then GSA can find the optimal parameters of the SVM by simulating the law of gravitation, and finding the optimum parameter eventually. Finally, a network security situation prediction model is established according to the optimal parameters. Using DARPA 1999 data set provided by MIT Lincoln Laboratory in MATLAB platform, the simulation results show that GSA-SVM improves the accuracy of network security situation prediction and accelerates prediction of network security situation relative to other prediction algorithms. This provides a new way to solve problem of network security situation prediction.