改进蜉蝣算法及其在防火墙策略配置中的应用

Improved mayfly algorithm and its application in firewall policy configuration

针对防火墙数据量大、人工配置效率低下的问题,提出一种改进的蜉蝣算法(IMA)来优化支持向量机(SVM)防火墙配置模型中的参数。改进的蜉蝣算法首先引入改进的Tent混沌映射来对蜉蝣种群进行初始化,其次在蜉蝣群体中引入遗传算法中的变异策略。将IMA运用于SVM防火墙配置模型参数寻优,通过对防火墙数据进行训练测试,结果表明,IMA的寻优性能较好,检测速度较快。

In view of the large amount of firewall data and the low efficiency of manual configuration,an improved mayfly algorithm(IMA)is proposed to optimize the parameters in the support vector machine(SVM)firewall configuration model.The IMA first introduces improved Tent chaotic mapping to initialize the mayfly population,and secondly,introduces the mutation strategy in the genetic algorithm into the mayfly population.IMA is applied to the optimization of SVM firewall configuration model parameters.Through training and testing of firewall data,the results show that the optimization performance of the model is better and the detection speed is fast.