A hybrid deep learning model for detecting DDoS flooding attacks in SIP-based systems

This publication has 21 references indexed in Scilit:

• A Bayesian change point model for detecting SIP-based DDoS attacks
  Digital Signal Processing, 2018
• An intelligent cyber security system against DDoS attacks in SIP networks
  Computer Networks, 2018
• Protecting from Cloud-based SIP flooding attacks by leveraging temporal and structural fingerprints
  Computers & Security, 2017
• Security and management framework for an organization operating in cloud environment
  Annals of Telecommunications, 2017
• Novel session initiation protocol-based distributed denial-of-service attacks and effective defense strategies
  Computers & Security, 2016
• Guest Editorial: Introduction to the Special Issue on Emerging Security Trends for Deeply-Embedded Computing Systems
  IEEE Transactions on Emerging Topics in Computing, 2016
• A comprehensive study of flooding attack consequences and countermeasures in Session Initiation Protocol (SIP)
  Security and Communication Networks, 2015
• DDoS Attack Detection Using Fast Entropy Approach on Flow- Based Network Traffic
  Procedia Computer Science, 2015
• SIP: Session Initiation Protocol
  2002
• Long Short-Term Memory
  Neural Computation, 1997