Collaborative prediction and detection of DDoS attacks in edge computing: A deep learning-based approach with distributed SDN
Authors:
- Hongliang ZhouHZHongliang Zhou
- Yifeng ZhengYZYifeng Zheng
- Xiaohua JiaXJXiaohua Jia
- Jiangang ShuJSJiangang Shu
Top Cited Papers
- 20 February 2023
- journal article
- research article
- Published by Elsevier in Computer Networks
- Vol. 225
Abstract
No abstract availableKeywords
Funding Information
- Basic and Applied Basic Research Foundation of Guangdong Province (2021A1515110027)
- Basic and Applied Basic Research Foundation of Guangdong Province (2023A1515010714)
- Science, Technology and Innovation Commission of Shenzhen Municipality (RCBS20210609103056041)
- Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20220531095416037)
This publication has 46 references indexed in Scilit:
- Detection and defense ofDDoSattack–based on deep learning inOpenFlow‐basedSDNInternational Journal of Communication Systems, 2018
- FADM: DDoS Flooding Attack Detection and Mitigation System in Software-Defined Networking2017
- SDN-Based DDoS Attack Detection with Cross-Plane Collaboration and Lightweight Flow Monitoring2017
- A Defense System for Defeating DDoS Attacks in SDN based Networks2017
- Distributed SDN controller system: A survey on design choiceComputer Networks, 2017
- Incremental Topology Transformation for Publish/Subscribe Systems Using Integer Programming2017
- Predicting network attack patterns in SDN using machine learning approach2016
- A survey of security solutions for distributed publish/subscribe systemsComputers & Security, 2016
- UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)2015
- Toward developing a systematic approach to generate benchmark datasets for intrusion detectionComputers & Security, 2012