Volume 4 , Issue 1 , PP: 08-18, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud A. Zaher 1 * , Mohmaed A. Labib 2
Doi: https://doi.org/10.54216/IJWAC.040101
Wireless sensor networks (WSN) is widely utilized for collecting data related to physical parameters from the environment. Security remains a challenging issue in the design of WSN. Security in WSN from Denial of Service (DoS) attack is an important security risk. This study introduces an artificial flora optimization algorithm with functional link neural network (AFOA-FLNN) model for DoS attack classification in WSN. The presented AFOA-FLNN model initially undergoes data pre-processing to transform the data into meaningful way. Secondly, the FLNN model is utilized for the effective recognition and classification of intrusions in WSN. Finally, the AFOA is exploited for optimally tuning the parameters involved in the FLNN model and results in enhanced performance. In order to demonstrate the better outcomes of the AFOA-FLNN model, a wide-ranging experimentation assessment on test data and the results pointed out the improved outcomes of the AFOA-FLNN model.
DoS attack, Intrusion, Security, Machine learning, Parameter optimization, WSN
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