Detecting and mitigating DDoS attacks with moving target defense approach based on automated flow classification in SDN networks
- 4 September 2023
- journal article
- research article
- Published by Elsevier in Computers & Security
- Vol. 134
Abstract
No abstract availableKeywords
This publication has 27 references indexed in Scilit:
- Detection of DDoS attacks with feed forward based deep neural network modelExpert Systems with Applications, 2021
- Evaluating the effectiveness of shuffle and redundancy MTD techniques in the cloudComputers & Security, 2021
- SDN-based cyber defense: A surveyFuture Generation Computer Systems, 2021
- Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDNFuture Generation Computer Systems, 2020
- A New Framework for DDoS Attack Detection and Defense in SDN EnvironmentIEEE Access, 2020
- Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directionsComputer Science Review, 2020
- New-flow based DDoS attacks in SDN: Taxonomy, rationales, and research challengesComputer Communications, 2020
- DDoS detection and defense mechanism based on cognitive-inspired computing in SDNFuture Generation Computer Systems, 2019
- Survey on SDN based network intrusion detection system using machine learning approachesPeer-to-Peer Networking and Applications, 2018
- SDN-Assisted Slow HTTP DDoS Attack Defense MethodIEEE Communications Letters, 2017