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

Authors
  • Nazih, Waleed
  • Hifny, Yasser
  • Elkilani, Wail S.
  • Dhahri, Habib1
  • Abdelkader, Tamer
  • 1 Faculty of Sciences and Technology, University of Kairouan, Sidi Bouzid 4352, Tunisia
Type
Published Article
Journal
Sensors
Publisher
MDPI AG
Publication Date
Oct 17, 2020
Volume
20
Issue
20
Identifiers
DOI: 10.3390/s20205875
PMID: 33080829
PMCID: PMC7589981
Source
PubMed Central
Keywords
License
Green

Abstract

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.

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