Improving the Routing Security in Wireless Sensor Networks using  Neutrosophic Set and Machine Learning Models

Hanadi Ahmad Simmak; Ahmed A El-Douh; Tareef S Alkellezli; Rabih Sbera; Darin shafek; Ahmed Abdelhafeez

Authors

Hanadi Ahmad Simmak Biomedical Engineering Department, Collage of Engineering, Ashur University, Baghdad, Iraq
Ahmed A El-Douh Information Systems Department, Faculty of Information Systems and Computer Science, October 6 University, Giza, 12585, Egypt; School of Cyber Science and Engineering, Huazhong University of Science and Technology, 1037, Hongshan, Wuhan 430074, China;Applied Science Research Center, Applied Science Private University, Amman
Tareef S Alkellezli Faculty of Informatics Engineering, Department of Computer Systems and Networks Engineering, University of Homs, Homs, Syria
Rabih Sbera College of Mechanical and Electrical Engineering, Computer and Automatic Control Engineering Department, Lattakia University, Lattakia, Syria
Darin shafek College of Mechanical and Electrical Engineering, Computer and Automatic Control Engineering Department, Lattakia University, Lattakia, Syria
Ahmed Abdelhafeez Computer Science Department, Faculty of Information Systems and Computer Science, October 6 University, Giza, 12585, Egypt; Applied Science Research Center, Applied Science Private University, Amman

Keywords:

Bipolar Neutrosophic Numbers; Uncertainty; Wireless Sensor Networks; Security. Attacks.

Abstract

Numerous methods have been put forth to identify and safeguard routing data because
Wireless Sensor Networks (WSNs) are susceptible to attacks during data transfer. To create an
artificial intelligence-based attack detection system for WSNs, we provide a unique stochastic
predictive machine learning technique in this research that is intended to identify unreliable
events and untrustworthy routing properties. Our approach makes use of real-time feature
analysis of simulated WSN routing data. We create a strong foundation for categorization. Our
approach's primary benefit is the development of an effective machine learning (ML) technique
that can analyze and filter WSN traffic to stop dangerous and suspicious data, lessen the
significant variation in the routing information gathered, and quickly identify assaults before they
happen. We use the XGBoost and Random Forest (RF) models with different parameters. Then
the bipolar neutrosophic set is used to deal with uncertainty and vague information. The
neutrosophic set is used to rank the ML models and select the best one.

DOI: 10.5281/zenodo.15733536