IP Flow Classification Based on GATS-C4.5

Flow classification plays an important role in the research field of network security monitoring,Quality of Service,and intrusion detection.Recently,there has been much interest in them.We proposed a wrapper feature selection algorithm GATS-C4.5 aiming at modeling lightweight flow classifier by(1)using hybrid genetic-tabu approach as search strategy to specify candidate subsets for evaluation;(2)using C4.5 algorithm as wrapper approach to obtain the optimum feature subset.We examined the feasibility of our algorithm by conducting some experiments on flow datasets.The experimental results show that classifier with our approach can greatly improve computational performance without negative impact on classification accuracy.Further more,our approach is able not only to have smaller resource consumption,but also to have higher classification accuracy than Nave Bayes method with Kernel density estimation after Fast Correlation-Based Filter(NBK-FCBF).