Journal of ISMAC
IRO Journals
Volume 4 — Issue 1 — March 2022

DDoS Detection using Machine Learning Techniques

https://doi.org/10.36548/jismac.2022.1.003
Pages: 24-32
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R. Amrish
Department of Electronics and Communication Engineering,Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai
K. Bavapriyan
Department of Electronics and Communication Engineering,Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai
V. Gopinaath
Department of Electronics and Communication Engineering,Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai
A. Jawahar
Department of Electronics and Communication Engineering,Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai
C. Vinoth Kumar
Department of Electronics and Communication Engineering,Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai
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How to Cite

Amrish, R., K. Bavapriyan, V. Gopinaath, A. Jawahar, and C. Vinoth Kumar. 2022. “DDoS Detection Using Machine Learning Techniques”. Journal of ISMAC 4 (1): 24-32. https://doi.org/10.36548/jismac.2022.1.003.
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Keywords

— Internet of Things (IoT)
— DDoS (Distributed Denial of Service)
— KNN
— ANN
— Random Forest
— Decision Tree
— Machine Learning
Published: 13-05-2022

Abstract

A Distributed Denial of Service (DDoS) attack is a type of cyber-attack that attempts to interrupt regular traffic on a targeted server by overloading the target. The system under DDoS attack remains occupied with the requests from the bots rather than providing service to legitimate users. These kinds of attacks are complicated to detect and increase day by day. In this paper, machine learning algorithm is employed to classify normal and DDoS attack traffic. DDoS attacks are detected using four machine learning classification techniques. The machine learning algorithms are tested and trained using the CICDDoS2019 dataset, gathered by the Canadian Institute of Cyber Security. When compared against KNN, Decision Tree, and Random Forest, the Artificial Neural Network (ANN) generates the best results.

References

  1. Alkasassbeh, M.; Al-Naymat, G.; Hassanat, A.B.; Almseidin, M. (2016) “Detecting Distributed Denial of Service Attacks Using Data Mining Techniques.” Int. J. Adv. Comput. Sci. Appl.
  2. D. V. V. S. Manikumar and B. U. Maheswari (2020), ‘‘Blockchain based DDoS mitigation using machine learning techniques’’ in Proc. 2nd Int. Conf. Inventive Res. Comput. Appl. (ICIRCA), pp. 794–800.
  3. DdoS Evaluation Dataset (CICDDoS2019). https://www.unb.ca/cic/datasets/ddos 2019.html
  4. Doshi, R., Apthorpe, N., & Feamster, N. (2018). “Machine Learning DDoS Detection for Consumer Internet of Things Devices”. IEEE Security and Privacy Workshops (SPW).
  5. Netscout Systems (2021)."Netscout https://www.netscout.com/threatreport Threat Intelligence Report".
  6. S. Wani, M. Imthiyas, H. Almohamedh, K. M. Alhamed, S. Almotairi and Y. Gulzar (2021), ‘‘Distributed denial of service (DDoS) mitigation using blockchain—A comprehensive insight’’ Symmetry, vol. 13, no. 2, p. 227.
  7. Saini, P. S., Behal, S., & Bhatia, S (2020). “Detection of DDoS Attacks using Machine Learning Algorithms”. 7th International Conference on Computing for Sustainable Global Development (INDIA.Com).pp;16-21,.
  8. Sharma, M.; Pant, S.; Kumar Sharma, D.; Datta Gupta, K.; Vashishth, V.; Chhabra, A (2020). “Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions.” In Transactions on Emerging Telecommunications Technologies; Wiley: Hoboken, NJ, USA; Volume 32, p. e4137.
  9. Singh, R., Tanwar, S., Sharma, T.P. (2020), “Utilization of Blockchain for mitigating the distributed denial of service attacks”. Secur. Priv. 3(3), 1–13.