Defensive mechanism against DDoS attack based on feature selection and multi-classifier algorithms

Anupama Mishra (), Neena Gupta () and Brij B. Gupta ()
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Anupama Mishra: Gurukul Kangri (Deemed to be University)
Neena Gupta: Gurukul Kangri (Deemed to be University)
Brij B. Gupta: International Center for AI and Cyber Security Research and Innovations, Asia University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2023, vol. 82, issue 2, No 4, 229-244

Abstract: Abstract Distributed denial of service attacks are common and very severe threat to various computing technology like Cloud, IoT and Blockchain because of the disruption they cause to the services that are provided. Many different types of DDoS attacks are there, each with a unique action, making it difficult for network monitoring and control systems to identify and prevent them. The objective of this research work is to explore and select a set of data to represent DDoS attack events and attack traffic information. A pre-processing phase is used to clean and transform the data, and afterwards the generation of a model of machine learning for multi-class classification is done. This is carried out to identify the various classification of different types of DDoS attacks. We have used CIC dataset for the experiment which contains all types of DDoS attack and huge in number of records. Random Forest, Support Vector Machine, Naive Bayes, Decision Tree, XGBoost, and AdaBoost are six different types of machine learning algorithms employed in this research. FRom the results, AdaBoost achieves the best accuracy of 99.87% in 27.4 s of computation time. Naive Bayes has the fastest computing time (3.2 s) with 94.15% accuracy, where as Support Vector Machine has the slowest time, a lazy learner (229m26s for training and 0.2 s for prediction) and has the low accuracy (95.73%).

Keywords: DDoS; Machine learning; Sci-learn; CICDoS2019; Data mining (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11235-022-00981-4

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