改进ACO-SVM在网络入侵检测中的应用

Improved ACO-SVM for Network Intrusion Detection

为了提高网络入侵检测正确率,降低特征冗余,提出一种蚁群优化与支持向量机相结合的入侵检测方法(ACO-SVM)。利用支持向量机的分类精度和特征子集维数加权构造综合适应度指标,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,并设计了局部细化搜索方式,实现特征选择结果降维,提高算法的收敛性。

In order to improve the detection accuracy network intrusion detection, this paper proposes a novel network intrusion detection method, namely the ACO-SVM which is based ant colony optimization algorithm and support vector machine to cope with feature selection issue for network intrusion detection. The classification accu-racy of support vector machine and the selected feature dimension form the fitness function, and the ant colony op-timization algorithm provides good global searching capability and multiple sub-optimal solutions, and a local re-finement searching scheme is designed to exclude the redundant features and improves the convergence rate. The experimental results show that the proposed method has reduced features dimensionality greatly and improve the detection accuracy of network intrusion.