Abebaw Demelash Gebeyehu

Mr. Abebaw Demelash Gebeyehu

MSc/BSc Computer Engineering
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Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 February 2026 in Future Internet
Verified
Journal Article
Open access
PDF file
YMYirga Yayeh Munaye
Yirga Yayeh Munaye
AGAbebaw Demelash Gebeyehu
Abebaw Demelash Gebeyehu
LTLi-Chia Jerry Tai
Li-Chia Jerry Tai
ZAZemenu Alem Abebe
Zemenu Alem Abebe
AWAeneas Bekele Workneh
Aeneas Bekele Workneh
Getaneh Berie TarekegnGetaneh Berie Tarekegn
Getaneh Berie Tarekegn
Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion detection framework optimized for WSN-based IoT environments. The proposed approach employs the WSN-DS benchmark dataset and integrates adaptive synthetic sampling (ADASYN) to address class imbalance, followed by a hybrid feature selection strategy combining Feature Importance Selection (FIS) and Recursive Feature Elimination (RFE) to reduce dimensionality and improve learning efficiency. An XGBoost classifier is then trained using five-fold cross-validation to ensure robust generalization. The experimental results demonstrate that the proposed framework significantly outperforms baseline methods, achieving an overall accuracy of 99.87%, with substantial gains in terms of F1-score, precision, and recall. Comparative analysis against recent WSN-DS studies confirms the effectiveness of combining imbalance correction, optimized feature selection, and ensemble learning. These findings highlight the potential of the proposed model as a lightweight and highly accurate intrusion detection solution for emerging WSN-IoT deployments.