DDoS Attack Detection in WSN using Modified Invasive Weed Optimization with Extreme Learning Machine

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i11s.8093
Keywords:
Wireless sensor networks, DDoS attack, Machine learning, Metaheuristics, Extreme learning machine

Main Article Content

C. Murugesh
Assistant Professor/Programmer, Department of Computer and Information Science, Annamalai University, Annamalai Nagar
S. Murugan
Assistant Professor, Dr. M.G.R. Government Arts and Science College for Women, Villupuram

Abstract

Wireless sensor networks (WSN) are the wide-spread methodology for its distribution of the vast amount of devoted sensor nodes (SNs) that is employed for sensing the atmosphere and gather information. The gathered information was transmitted to the sink nodes via intermediate nodes. Meanwhile, the SN data are prone to the internet, and they are vulnerable to diverse security risks, involving distributed denial of service (DDoS) outbreaks that might interrupt network operation and compromises data integrity. In recent times, developed machine learning (ML) approaches can be applied for the discovery of DDoS attacks and accomplish security in WSN. To achieve this, this study presents a modified invasive weed optimization with extreme learning machine (MIWO-ELM) model for DDoS outbreak recognition in the WSN atmosphere. In the presented MIWO-ELM technique, an initial stage of data pre-processing is conducted. The ELM model can be applied for precise DDoS attack detection and classification process. At last, the MIWO method can be exploited for the parameter tuning of the ELM model which leads to improved performance of the classification. The experimental analysis of the MIWO-ELM method takes place using WSN dataset. The comprehensive simulation outputs show the remarkable performance of the MIWO-ELM method compared to other recent approaches.

Article Details

How to Cite
Murugesh, C. ., & Murugan, S. . (2023). DDoS Attack Detection in WSN using Modified Invasive Weed Optimization with Extreme Learning Machine. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 215–221. https://doi.org/10.17762/ijritcc.v11i11s.8093
Issue
Vol. 11 No. 11s (2023)
Section
Articles

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