English

Abstract

Traffic classification technique is an important tool for network and system security in the environments such as cloud computing based environment. Modern traffic classification methods plans to take the gain of flow statistical features and machine learning methods, but the classification performance is affected by reduced supervised information, and unfamiliar applications. In addition detection of anomalies in the flow level is not considered in earlier approaches. Current work proposes Flow-level anomaly detection with the framework of Unknown Flow Detection approaches. Flow-level anomaly can be detected by using Synthetic flow-level traffic trace generation approach(SG -FLT). The two major challenges with such an approach are to characterize normal and anomalous network behavior, and to discover realistic models defining normal and anomalous traffic at the flow level. Unknown flow detection approach has been performed by Flow level propagation and finding the correlated flows to boost the classification accuracy. Performance evaluation is conducted on real-world network traffic datasets which demonstrates that the proposed scheme provides efficient performance than existing methods in the complex network environment.