- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Optimal detection of attacks in wireless sensor networks using deep learning and Bayesian optimization | ||
| Al-Qadisiyah Journal for Engineering Sciences | ||
| Article 8, Volume 19, Issue 2, June 2026, Pages 220-224 PDF (6.66 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30772/qjes.2024.155627.1431 | ||
| Authors | ||
| Zahraa Mehssen A. Al-Hamdawee* 1; Humam H. Mightadh1; Sayyed M. Mazinani* 2 | ||
| 1Department of Electrical Engineering, College of Engineering, University of Misan, Amarah 62001, Iraq. | ||
| 2Department of Computer and Electrical Engineering, Imam Reza International University, Mashhad, Iran. | ||
| Abstract | ||
| A B S T R A C T Diagnosing attacks in wireless sensor networks (WSN) is a crucial concern in cyber security that impacts diagnosis models' accuracy because of data imbalance and outliers existence. The present paper aims to design and perform a novel strategy for efficient attack diagnosis in WSN that applies DL models’ integration (DNN and CNN+LSTM) and Bayesian optimization. The present study checks concerns on data analysis such as various attack complexities and instances’ imbalance. Such concerns have been mentioned by applying techniques such as MinMax scaling, data balancing with ADASYN, polynomial feature engineering, and outlier elimination with DBSCAN. The outcomes of the experiments illustrate that the DNN model obtained an F1-Score of 85.71%, and appeared an accuracy of 99.14% which is an important development across conventional techniques. Such results illustrate that was presented technique could develop WSN security and possesses high capability for various attacks’ kinds’ diagnosis. © 2024 University of Al-Qadisiyah. All rights reserved. | ||
| Keywords | ||
| Attack detection; Bayesian methods; Data balancing; Deep neural network; Convolutional neural network; Long short-term memory | ||
|
Statistics Article View: 282 PDF Download: 249 |
||