Research of IP Flow Classification Based on Heuristic Search
The accuracy of IP flow classification based on the characteristics of the application layer is relatively high,but it will cost a lot of time to match the feature library when the feature library is huge.To solve this problem,this paper proposes an approach of traffic classification that combines the characteristics of the application layer with heuristic search.First,we extract the common features from the packets generated by a variety of applications to establish the heuristic rules.Second,we divide the feature library into several feature subsets according to heuristic rules.Then in the process of traffic classification,we only need to match a specific feature subset according to heuristic rules,so the matching of irrelevant features can be greatly reduced,the feature subset is more targeted to be matched and the time performance is improved.For some applications we use DNS as a guide in traffic classification,overcoming the drawback that the encrypted data can not be identified based on the characteristics of the application layer.This paper realizes the algorithm with C language and compares it with l7-filter.The experiments show that the offline classification speed of the method presented in this paper is as 6-10 times as l7-filter,and the accuracy of identifying traffic of various application in our method can reach more than 98%.