Detection of Distributed Denial of Service Attacks based on Machine Learning Algorithms


Md Abdur Rahman,Associate Professor of Computer science, Department of Mathematics, Jahangirnagar university, Savar, Dhaka, Bangladesh


Distributed Denial of Service (DDoS) attacks make the challenges to provide the services of the data resources to the web clients. In this paper, we concern to study and apply different Machine Learning (ML) techniques to separate the DDoS attack instances from benign instances. Our experimental results show that forward and backward data bytes of our dataset are observed more similar for DDoS attacks compared to the data bytes for benign attempts. This paper uses different machine learning techniques for the detection of the attacks efficiently in order to make sure the offered services from web servers available. This results from the proposed approach suggest that 97.1% of DDoS attacks are successfully detected by the Support Vector Machine (SVM). These accuracies are better while comparing to the several existing machine learning approaches.



Machine learning, Machine learning algorithms, DDoS attack, Benign Attempts, Confusion matrix


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  • APA:
    Rahman,M.A.(2020). Detection of Distributed Denial of Service Attacks based on Machine Learning Algorithms. International Journal of Smart Home, 14(2), 15-24. 10.21742/IJSH.2020.14.2.02
  • Harvard:
    Rahman,M.A.(2020). "Detection of Distributed Denial of Service Attacks based on Machine Learning Algorithms". International Journal of Smart Home, 14(2), pp.15-24. doi:10.21742/IJSH.2020.14.2.02
  • IEEE:
    [1] M.A.Rahman, "Detection of Distributed Denial of Service Attacks based on Machine Learning Algorithms". International Journal of Smart Home, vol.14, no.2, pp.15-24, Oct. 2020
  • MLA:
    Rahman Md Abdur. "Detection of Distributed Denial of Service Attacks based on Machine Learning Algorithms". International Journal of Smart Home, vol.14, no.2, Oct. 2020, pp.15-24, doi:10.21742/IJSH.2020.14.2.02


  • Volume 14, No. 2, 2020
  • ISSN(p):1975-4094
  • ISSN(e):2383-725X
  • Published:Oct. 2020