Unsupervised traffic classification using flow statistical properties and IP packet payload

Unsupervised traffic classification using flow statistical properties and IP packet payload. The researchers have started looking for Internet traffic recognition techniques that are independent of `well known’ TCP or UDP port numbers or interpreting the contents of packet payloads. Newer approaches classify traffic by recognizing statistical patterns in externally observable attributes of the traffic (such as typical packet lengths and inter-arrival times). The main goal is to cluster or classify the Internet traffic flows into groups that have identical statistical properties. The need to deal with Traffic patterns, large < Final Year Projects > datasets and Multidimensional spaces of flow and packet attributes is one of the reasons for the introduction of Machine Learning (ML) techniques in this field. ML techniques are subset of Artificial Intelligence used for traffic recognition. Further, there are four types of Machine Learning, i.e. Classification (Supervised learning), clustering (Un-Supervised learning), Numeric prediction and Association. In this research paper IP traffic recognition through classification process is implemented.

Abstract— Unsupervised traffic classification using flow statistical properties and IP packet payload.