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

Author

Listed:
  • 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
    Asia University
    Lebanese American University
    Center for Interdisciplinary Research at University of Petroleum and Energy Studies (UPES))

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%).

Suggested Citation

  • Anupama Mishra & Neena Gupta & Brij B. Gupta, 2023. "Defensive mechanism against DDoS attack based on feature selection and multi-classifier algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(2), pages 229-244, February.
    • Handle: RePEc:spr:telsys:v:82:y:2023:i:2:d:10.1007_s11235-022-00981-4
    DOI: 10.1007/s11235-022-00981-4
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    References listed on IDEAS

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    1. Anupama Mishra & Neena Gupta & B. B. Gupta, 2021. "Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX controller," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 47-62, May.
    2. Sanaa Kaddoura & Ramzi A. Haraty & Karam Al Kontar & Omar Alfandi, 2021. "A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems," Future Internet, MDPI, vol. 13(4), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    DDoS; Machine learning; Sci-learn; CICDoS2019; Data mining;
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