基于ACO-FS-SVM特征选择加权的网络入侵分类方法

特征选择和分类器设计是网络入侵分类的关键,为了提高网络入侵分类率,针对特征选择问题,提出一种蚁群算法优化SVM选择和加权特征的网络入侵分类方法.首先利用支持向量机的分类精度和特征子集维数加权构造了综合适应度指标,然后利用蚁群算法的全局寻优和多次优解搜索能力实现特征子集搜索;然后选择网络数据的关键特征,计算信息增益获得各个特征权重,并根据特征权重构建加权支持向量机的网络入侵分类器;最后设计了局部细化搜索方式,使得特征选择结果不含冗余特征的同时提高了算法的收敛性,并通过KDD1999数据集验证了算法有效性.结果表明,ACO-SVM有效降低了特征维数,提高了网络入侵检测正确率和检测速度.

Network Attacking Classification Method Based on Features Selection by ACO and SVM

Feature selection and classifier design is the key of network attacking detection.In order to improve the detection accuracy network attacking detection,a novel network attacking detection method is proposed,namely the ACO-SVM which is based ant colony optimization algorithm and support vector machine to cope with feature selection issue for network attacking detection.The classification accuracy of support vector machine and the selected feature dimension form the fitness function,and the ant colony optimization algorithm provides good global searching capability and multiple sub-optimal solutions,and a local refinement searching scheme is designed to exclude the redundant features and improves the convergence rate.The performance of method are test by KDD1999 data,the experimental results show that the proposed method has reduced features dimensionality greatly and improve the detection accuracy of network attacking as well as the significant improvement on detection speed.