@inproceedings{f9feff06127a4fefbd02248010fdf937,
title = "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.",
keywords = "Big Data, Hadoop, Spark, Tunneling, VoIP",
author = "Mazhar Rathore and Anand Paul and Awais Ahmad and Muhammad Imran and Mohsen Guizani",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 41st IEEE Conference on Local Computer Networks, LCN 2016 ; Conference date: 07-11-2016 Through 10-11-2016",
year = "2016",
month = dec,
day = "22",
doi = "10.1109/LCN.2016.128",
language = "English",
series = "Proceedings - Conference on Local Computer Networks, LCN",
publisher = "IEEE Computer Society",
pages = "595--598",
booktitle = "Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016",
address = "United States",
}