蚁群优化支持向量机的通信信息安全态势预测

Information Security Situation Prediction of Communication Based on Ant Colony Optimization Support Vector Machine

态势预测可以帮助管理人员了解通信信息的安全状态,支持向量机在通信信息安全态势预测中存在参数优化的难题,为了解决该难题,提高通信信息安全态势预测精度,设计基于蚁群优化支持向量机的通信信息安全态势预测模型。采集通信信息安全态势的历史数据,并通过相空间重构对通信信息安全态势数据进行处理;将重构后的通信信息安全态势数据输入到支持向量机中进行学习,并通过蚁群算法对支持向量机参数进行不断调整,尽量提高通信信息安全态势预测的准确性;与传统支持向量机的通信信息安全态势预测模型进行对比测试。测试结果表明,蚁群优化支持向量机的通信信息安全态势预测精度明显高于传统支持向量机,减少了通信信息安全态势预测误差,可以为通信信息安全管理人员提供有价值的参考信息。

Situation prediction can help administrators to understand the future security status of communication information.Support vector machine(SVM)has the problem of parameter optimization in communication information security situation prediction.In order to solve this problem and improve the accuracy of communication information security situation prediction,a communication information security situation prediction model based on ant colony optimization support vector machine is designed.Historical data of communication information security situation is collected,communication information security situation data is reconstructed through phase space reconstruction,and data is input into support vector machine for learning.The parameters of support vector machine are continuously adjusted through ant colony algorithm to improve the accuracy of communication information security situation prediction.It is compared with the communication information security situation prediction model of traditional support vector machine.The results show that the prediction accuracy of communication information security situation of ant colony optimization support vector machine is significantly higher than that of traditional support vector machine,which reduces the error of communication information security situation prediction,and can provide valuable reference information for communication information security managers.