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Related Experiment Video

Updated: Dec 5, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks.

Waleed Nazih1,2, Yasser Hifny3, Wail S Elkilani2,4

  • 1College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for detecting Distributed Denial of Service (DDoS) attacks on Voice over IP (VoIP) systems using Recurrent Neural Networks (RNNs). The approach enhances security by accurately identifying these harmful attacks with minimal delay.

Keywords:
deep learningdistributed denial of service attacksnetwork securityrecurrent neural networkssession initiation protocolvoice over IP

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Voice over IP (VoIP) systems are increasingly adopted, but are vulnerable to security threats.
  • Session Initiation Protocol (SIP), a core VoIP protocol, lacks inherent security features.
  • Distributed Denial of Service (DDoS) attacks disrupt services by overwhelming VoIP systems.

Purpose of the Study:

  • To develop an effective method for detecting DDoS attacks in VoIP networks.
  • To enhance feature extraction from SIP messages for improved attack classification.
  • To evaluate the performance of a deep learning model for DDoS detection.

Main Methods:

  • Formulating DDoS detection as a classification problem.
  • Utilizing token embedding to enhance features extracted from SIP messages.
  • Implementing a deep learning model based on Recurrent Neural Networks (RNNs).
  • Validating the system with a real traffic dataset including diverse attack scenarios.

Main Results:

  • The proposed system achieves high detection accuracy for DDoS attacks.
  • The system demonstrates a low detection time.
  • Detection accuracy for low-rate attacks surpasses traditional machine learning methods.
  • The RNN-based model effectively handles both low and high-rate DDoS attacks.

Conclusions:

  • The token embedding and RNN-based approach offers a robust solution for VoIP security.
  • This method provides a significant advancement in detecting DDoS attacks, particularly low-rate variants.
  • The findings contribute to securing VoIP communications against evolving cyber threats.