Detecting flooding DDoS attacks in software defined networks using supervised learning techniques
Authors:
- Song WangSWSong Wang
- Juan Fernando BalarezoJBJuan Fernando Balarezo
- Karina Gomez ChavezKCKarina Gomez Chavez
- Akram Al-HouraniAAAkram Al-Hourani
- Sithamparanathan KandeepanSKSithamparanathan Kandeepan
- Muhammad Rizwan AsgharMAMuhammad Rizwan Asghar
- Giovanni RusselloGRGiovanni Russello
Open Access
- 3 June 2022
- journal article
- research article
- Published by Elsevier in Engineering Science and Technology, an International Journal
- Vol. 35
Abstract
No abstract availableKeywords
This publication has 27 references indexed in Scilit:
- OpenSec: Policy-Based Security Using Software-Defined NetworkingIEEE Transactions on Network and Service Management, 2016
- Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and ChallengesIEEE Communications Surveys & Tutorials, 2015
- Self-adaptive attribute weighting for Naive Bayes classificationExpert Systems with Applications, 2015
- Software-Defined Networking: A Comprehensive SurveyProceedings of the IEEE, 2014
- A Survey on Software-Defined NetworkingIEEE Communications Surveys & Tutorials, 2014
- A Survey of Software-Defined Networking: Past, Present, and Future of Programmable NetworksIEEE Communications Surveys & Tutorials, 2014
- Toward developing a systematic approach to generate benchmark datasets for intrusion detectionComputers & Security, 2012
- Exploratory Undersampling for Class-Imbalance LearningIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008
- An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection2003
- A comparison of methods for multiclass support vector machinesIEEE Transactions on Neural Networks, 2002