Mitigation of Cyber Attacks in SDN-Based IoT Systems Using Machine Learning Techniques

Authors

  • Kamal Singh IT Architect, IPGSL, UK, Manav Rachna International Institute of Research and Studies, India
  • Brijesh Kumar Dean Academics, Manav Rachna International Institute of Research and Studies, India
  • Sunil Kumar Executive Director, KPMG, Malaysia
  • Veer Pratap Singh IT Architect, IPGSL, UK
  • Ashanendra Singh Principal Consultant, Wipro, Australia

Keywords:

Cyber Threats, SDN, DDOS, IOT, Network Security

Abstract

Complex Distributed Denial-of-Service (DDoS) security assaults threaten the expansion of intelligent network infrastructure for the Internet of Things (IoT). The IoT cannot be protected by the enterprise network security solutions currently in use because they are too expensive. Integrating newly developed software-defined networking (SDN) technology effectively mitigates a computational load on an IoT network device, enabling the implementation of supplementary security measures. Because it is utilized in the precursor stage of the design for SDN-enabled IoT networks. However, sampling-based security offers poor DDoS attack detection accuracy. This study aims to investigate cognitive techniques for detecting and mitigating cyber risks in software-defined and contemporary network applications. SDN is a modern technology network that allows for centralized control and cyber threat detection with built-in machine learning techniques for increasing the adoption of (IoT) devices. SDN applications have become vulnerable to cyber threats. To ensure the security of these applications, detection and mitigation of cyber threats are crucial. Adopting SDN can result in benefits, including increased manageability, scalability, and overall performance. However, SDN poses issues, primarily if it is controlled and open to DDoS attacks. Machine learning-based models were employed in this specific research project to identify DDoS attacks in SDN. Based on the research results, the KNN classifier, in combination with the wrapper feature, leads to the most fantastic accuracy rate of about 98.3% in detecting attacks.

The results of this study indicate that in addition to the anticipated reduction in processing burdens, feature selection and machine learning techniques can enhance DDoS attack detection in SDN.

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References

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ONOS SDN Controller https://opennetworking.org/onos/

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Published

13.12.2023

How to Cite

Singh, K. ., Kumar, B. ., Kumar, S. ., Singh, V. P. ., & Singh, A. . (2023). Mitigation of Cyber Attacks in SDN-Based IoT Systems Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 482–492. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4149

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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