Network monitoring system using machine learning comparative analysis of classification techniques for network traffic monitoring

Online network traffic classification continues to be the focus of long-term interest. Network traffic monitoring and analysis can be done for many different reasons. Generally, it provides raw data input for network monitoring, Quality of Service (QoS) and intrusion detection. Specifically, network traffic monitoring enables the network analyst to understand network resources use and identify network performance. With this information, network analyst may adjust QoS policies to control and manage network resources. This aim is achieved by setting priorities for specific types of data in the network and logging the traffic to comply with the regulations. Network traffic monitoring can be used to create models for academic research. In this thesis, a machine-learning approach that accurately classifies network traffic using Decision Tree Algorithm (DT) is presented and implementing the Principal Component Analysis (PCA) Algorithm for reduction, side by side, to reach the best optimization. Machine learning technology will generate better solutions to monitor and classify network traffic as a result of highly accurate data mining technics and advanced statistics. The purpose of this thesis is to build a Network Monitoring System (NMS) using modern machine learning technologies that works in both online and offline modes. DT algorithm; one of the available data mining algorithms; is used to build the classifier of network. The experiment's results showed that NMS based system has 97.7486 % accuracy (ACC) in successfully classifying the network traffic.