Use of SPAN for Identified Network Traffic via Machine Learning

Abstract

Few years back the number of wireless devices and their use in our daily life has been increased a lot. All devices cell phones, laptops, tablets, camera, TVs, home appliances have become a part of network now. As the network devices are growing and getting connected to each other the security risks are getting higher. All the companies and organizations are now establishing and implanting the public and private wireless networks. Organization have to pay heavy cost to implement and integrate all devices together on a network. As wireless networks are more vulnerable to threats and in security's a huge network all the devices should be identified whenever they enter or leave a network traffic pool the experimental work in this paper will elaborate the methods to identify the network traffic identification under encryption. This paper emphases on identification of devices based on layer 2 functionality by MAC (Media Access Code). Later on, the identification was improved using labeled or tagged traffic methods by use of SPAN (Switch port analyzer technique) technology or protocol with assistance of
Virtual Local Area Network. Many Supervised learning methods were examined during experiment and were referenced on data collected by real time traffic. The network traffic of multiple deceives gradually passes through network so incremental learning method is implemented as classification for streaming data.