MACHINE LEARNING BASED NETWORK TRAFFIC CLASSIFICATION INCLUDING ZERO DAY TRAFFIC AS A CLASS.

Support Vector Machines (SVM)represent one of the most promising Machine Learning (ML) tools that can be applied to the problem of traffic classification in IP networks. In the case of SVMs, there are still open questions that need to be addressed before they can be generally applied to traffic classifiers. Identifying and categorizing network traffic by application type is challenging because of the continued evolution of applications, especially of those with a desire to be undetectable. The diminished effectiveness of port-based identification and the overheads of deep packet inspection approaches motivate us to classify traffic by exploit To tackle this critical problem, we propose a novel traffic classification scheme which has the capability of identifying zero-day traffic as well as accurately classifying the traffic generated bypre-defined application classes.