High-Speed Network Traffic Analysis: Detecting VoIP Calls in Secure Big Data Streaming

Abstract

Internet service providers (ISPs) and telecommunication authorities are interested in detecting VoIP calls either to block illegal commercial VoIP or prioritize the paid users VoIP calls. Signature-based, port-based, and pattern-based VoIP detection techniques are not more accurate and not efficient due to complex security and tunneling mechanisms used by VoIP. Therefore, in this paper, we propose a rule-based generic, robust, and efficient statistical analysis-based solution to identify encrypted, non-encrypted, or tunneled VoIP media (voice) flows using threshold approach. In addition, a system is proposed to efficiently process high-speed real-time network traffic. The accuracy and efficiency evaluation results and the comparative study show that the proposed system outperforms the existing systems with the ability to work in real-time and high-speed Big Data environment.