Classification and Analysis of Malicious Traffic with Multi-layer Perceptron Model

Abstract

Traffic classification is very important field of computer science as it provides network management information. Classification of traffic become complicated due to emerging technologies and applications. It is used for Quality of Service (QoS) provisioning, security and detecting intrusion in a system. In the past used of port, inspecting packet, and machine learning algorithms have been used widely, but due to the sudden changes in the traffic, their accuracy was diminished. In this paper a Multi-Layer Perceptron model with 2 hidden layers is proposed for traffic classification and target traffic classify into different categories. The experimental results indicate that proposed classifier efficiently classifies traffic and achieves 99.28% accuracy without feature engineering.