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Open AccessArticle

Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks

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College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
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Faculty of Computers and Information Sciences, Ain Shams University, Abassia, Cairo 11566, Egypt
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Faculty of Computers and Information, Helwan University, Ain Helwan, Cairo 11795, Egypt
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College of Applied Computer Sciences (CACS), King Saud University, Riyadh 11543, Saudi Arabia
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Faculty of Sciences and Technology, University of Kairouan, Sidi Bouzid 4352, Tunisia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5875; https://doi.org/10.3390/s20205875
Received: 10 September 2020 / Revised: 10 October 2020 / Accepted: 13 October 2020 / Published: 17 October 2020
(This article belongs to the Special Issue Intelligent and Adaptive Security in Internet of Things)
Many companies have transformed their telephone systems into Voice over IP (VoIP) systems. Although implementation is simple, VoIP is vulnerable to different types of attacks. The Session Initiation Protocol (SIP) is a widely used protocol for handling VoIP signaling functions. SIP is unprotected against attacks because it is a text-based protocol and lacks defense against the growing security threats. The Distributed Denial of Service (DDoS) attack is a harmful attack, because it drains resources, and prevents legitimate users from using the available services. In this paper, we formulate detection of DDoS attacks as a classification problem and propose an approach using token embedding to enhance extracted features from SIP messages. We discuss a deep learning model based on Recurrent Neural Networks (RNNs) developed to detect DDoS attacks with low and high-rate intensity. For validation, a balanced real traffic dataset was built containing three attack scenarios with different attack durations and intensities. Experiments show that the system has a high detection accuracy and low detection time. The detection accuracy was higher for low-rate attacks than that of traditional machine learning. View Full-Text
Keywords: deep learning; recurrent neural networks; voice over IP; session initiation protocol; network security; distributed denial of service attacks deep learning; recurrent neural networks; voice over IP; session initiation protocol; network security; distributed denial of service attacks
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Nazih, W.; Hifny, Y.; Elkilani, W.S.; Dhahri, H.; Abdelkader, T. Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks. Sensors 2020, 20, 5875.

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