Using Burstiness for Network Applications Classification

Abstract

Network traffic classification is a vital task for service operators, network engineers, and security specialists to manage network traffic, design networks, and detect threats. Identifying the type/name of applications that generate traffic is a challenging task as encrypting traffic becomes the norm for Internet communication. Therefore, relying on conventional techniques such as deep packet inspection (DPI) or port numbers is not efficient anymore. This paper proposes a novel flow statistical-based set of features that may be used for classifying applications by leveraging machine learning algorithms to yield high accuracy in identifying the type of applications that generate the traffic. The proposed features compute different timings between packets and flows. This work utilises tcptrace to extract features based on traffic burstiness and periods of inactivity (idle time) for the analysed traffic, followed by the C5.0 algorithm for determining the applications that generated it. The evaluation tests performed on a set of real, uncontrolled traffic, indicated that the method has an accuracy of 79% in identifying the correct network application.

Grano-GT: A granular ground truth collection tool for encrypted browser-based Internet traffic

“…where 𝛿𝑡 𝑖 = element in the time_delta list from L1 service filter, Based on our study, application service traffic occurs in a burst that is recognizable within the standard deviation of its interarrival times. This is also in line with a recent study that identified application traffic through its burstiness [33]. However, it is also worth noting that there is a micro delay between exact service execution and packet capture time.…”