Abstract:
Ever since the boom of social media, the internet has drastically changed our lives. From
social media platforms, Industry 4.0, Big Data, Cloud Computing to Augmented and
Virtual reality, the amount of data on the internet has increased on an unprecedented
scale, and it is not going to decrease in the years to come. This increasing amount
of data puts a huge strain on today’s network infrastructure. Internet Network Traffic
Classification is thus a new paradigm in which lightweight sophisticated algorithms are
designed and tested on live networks. These algorithms help in setting the forwarding
rules in a controller in a Software-Defined-Network based environment. In regard to
increasing audio and video streaming services, this problem has been focused a lot with
the help of Machine and Deep learning. These models do provide a high accuracy of
classification, but can become complex problems depending upon the specific application. This paves the way for the need of practical, trivial architectures. Thus, in this
thesis, a novel Fuzzy Logic based Lightweight Classification Architecture is proposed
and implemented to successfully identify and segregate Audio and Video from other
Network Traffic (NT) applications. First, a traffic classification scheme is devised and
formulated using a basic handful of features. The details of these features from a variety of applications including audio and video is elaborated. These features are used
later on to build the fuzzy based classification model. The classification model will then
distinguish audio and video based interactive and streaming services from other random
internet traffic based on rules set by Fuzzy that mimic human intuition. To verify the
proposed algorithm, flow-based network captures is used to identify whether the correct
class of network traffic is predicted or not. The results obtained from the Fuzzy logic is
also compared with traditional Polynomial regression and kNN based Machine Learning
algorithms to showcase the superiority of Fuzzy logic.
Keywords: Internet Traffic Classification, Fuzzy Logic
