Internet traffic classification via time-frequency analysis

Abstract

Concepts and technologies disclosed herein are directed to internet traffic classification via time-frequency analysis. According to one aspect of the concepts and technologies disclosed herein, a security classification scheme can be implemented to identify potentially malicious activities from normal internet traffic. The security classification scheme can exploit the distinctive characteristics of different types of traffic in both frequency domain and time domain to identify four different cases. Due to the separation of different types of traffic, the security classification scheme can lower the false alarm rate and improve network security. The security classification scheme can utilize a recursive discrete Fourier transform ('DFT') implementation to enhance computational efficiency. The security classification scheme can be deployed for real-time network traffic monitoring due to an efficient streaming design and can be effectively used to detect and predict when and where the suspicious activities occur within a monitored network.