A Multi - Controller SDN Framework for Advanced Attack Detection and Mitigation in IoT Environment

Roselin Lourd. J, Dineshkumar. T and Kaviarasan. S

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 3, 71-79

Abstract: This research introduces a pioneering Multi-Controller Software-Defined Networking (SDN) framework meticulously designed for bolstering attack detection and mitigation capabilities within the intricate landscape of Internet of Things (IoT) environments. Leveraging the random forest algorithm, this framework significantly enhances the ability to detect and counter Distributed Denial of Service (DDoS) attacks, a prevalent threat within IoT networks. The proposed Multi-Controller SDN framework orchestrates multiple controllers to collaborate seamlessly, ensuring distributed intelligence and effective collaboration across diverse IoT devices. The integration of the random forest algorithm enables robust and accurate identification of anomalous traffic patterns indicative of potential DDoS attacks. Key aspects of this research include the development and implementation of a scalable and resilient SDN framework capable of dynamically responding to evolving attack vectors within IoT ecosystems. The random forest algorithm, known for its ensemble learning capabilities and adaptability to diverse datasets, is specifically tailored to detect subtle anomalies and patterns associated with DDoS attacks amidst the complexities of IoT traffic. Through comprehensive simulations and practical evaluations, the framework's efficacy in detecting DDoS attacks is rigorously assessed. Results showcase the framework's ability to accurately identify and mitigate DDoS threats in near real-time, demonstrating its potential to fortify IoT networks against such malicious intrusions. This research contributes a novel approach harnessing the power of Multi-Controller SDN architecture and the random forest algorithm to advance the security posture of IoT environments. The proposed framework not only addresses the intricate challenges posed by DDoS attacks but also paves the way for adaptive and proactive defense mechanisms in the ever-evolving landscape of IoT security.

Keywords: Multi-Controller SDN; Internet of Things; Distributed Denial of Service attacks; Random Forest Algorithm; Attack Detection; Attack Mitigation, Security; Ensemble Learning; Scalability; Resilience; Adaptive Defense; Proactive Defense (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST24112173 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST24112173/IJSRST24112173 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i3:id:171

DOI: 10.32628/IJSRST24112173

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().