Abstract: Underwater Wireless Sensor Networks (UWSNs) are commonly employed for exploring and exploiting aquatic areas, and its role is very important and more beneficial precisely in hostile and constrained marine environments. However, their security is more critical than terrestrial wireless sensor networks (TWSNs) due to the space in which they are deployed, the wireless communication medium, and the cost of damage repair, and their protection is a problematic issue that needs to be continuously resolved. Consequently, it is highly recommended see required to take procedure to protect UWSNs against attacks and intrusion and maintain service quality. In general, existing works on machine learning-based intrusion detection system (IDS) and cyber-attack detection approaches for (UWSNs) utilize dedicated datasets designed for (WSNs) without adapting them to the aquatic environment. Furthermore, these studies analyze the enhancement of UWSN performance based on network metrics separately from machine learning model metrics, and vice versa. In this way, this paper proposes a novel cybersecurity detection approach-based model learning Histogram Gradient Bosting (HGB) classifier called (HGBoostUCAD). It classifies four types of DoS attacks (Blackhole, Grayhole, Flooding, and Scheduling), employing an adjusted dataset for (IDS) in wireless sensor networks (WSN-DS) taking into account simulate realistic environmental factors: salinity, temperature, deep through Mackenzie’s equation and node movement in training data. The insight of simulation results obtained, shows that our method reached 97% as accuracy and 96% as precision also outperformed both Deep Neural Network (DNN) as well as the recent study Hyper_RNN_SVM eventually referenced in this research, in terms of machine learning model metrics. In addition to machine learning model metrics, our approach provides network measurements by DoS attack type.

Hamid OUIDIR, Amine BERQIA and Siham AOUAD. “Histogram Gradient Boosting Classifier-Based UWSN Cyber Attack Detection Incorporating Environmental Factors (HGBoostUCAD)”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161217

@article{OUIDIR2025,
title = {Histogram Gradient Boosting Classifier-Based UWSN Cyber Attack Detection Incorporating Environmental Factors (HGBoostUCAD)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161217},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161217},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Hamid OUIDIR and Amine BERQIA and Siham AOUAD}
}