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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Audah, Lukman
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Computer Science & IT Electrical & Electronics Engineering

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Design and implementation of a centralized approach for multi-node localization Hasan, Ola A.; T. Rashid, Abdulmuttalib; S. Ali, Ramzy; Qasim, Hamza H.; A. Al Sibahee, Mustafa; Audah, Lukman
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2477-2487

Abstract

In this paper, a centralized approach for multi nodes localization is introduced. This approach is based on using a beacon fixed at the lower middle edge of the environment. This beacon is provided with a distance sensor and can scan the environment to measure the distance between the detecting node and the beacon. Also, remote control is fixed on the beacon to distinguish the identity of the detecting node. Two nodes are used in this approach, each node contains eight cells, and each cell has a 5 mm IR transmitter and TSOP4P38 IR receiver. If any one of the IR receivers has received the beacon ID, the transmitter which belongs to the same cell will respond by sending the node ID to the beacon. The beacon measurements and the information received from the detected nodes are then used to estimate the location and orientation of the visible nodes and the results will be saved in the main computer. Several experimental results have been tested with different distances from the nodes to the beacon. Also, different rotation angles at the beacon have been experienced to analyze the performance of the introduced approach.
Enhancing SDN security using ensemble-based machine learning approach for DDoS attack detection Hirsi, Abdinasir; Audah, Lukman; Salh, Adeb; Alhartomi, Mohammed A.; Ahmed, Salman
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1073-1085

Abstract

Software-defined networking (SDN) is a groundbreaking technology that transforms traditional network frameworks by separating the control plane from the data plane, thereby enabling flexible and efficient network management. Despite its advantages, SDN introduces vulnerabilities, particularly distributed denial of service (DDoS) attacks. Existing studies have used single, hybrid, and ensemble machine learning (ML) techniques to address attacks, often relying on generated datasets that cannot be tested because of accessibility issues. A major contribution of this study is the creation of a novel, publicly accessible dataset, and benchmarking the proposed approach against existing public datasets to demonstrate its effectiveness. This paper proposes a novel approach that combines ensemble learning models with principal component analysis (PCA) for feature selection. The integration of ensemble learning models enhances predictive performance by leveraging multiple algorithms to improve accuracy and robustness. The results showed that the ensemble of random forests (ENRF) model achieved the highest performance across all metrics with 100% accuracy, precision, recall, and F1-score. This study provides a comprehensive solution to the limitations of existing models by offering superior performance, as evidenced by the comparative analysis, establishing this approach as the best among the evaluated models.
Co-Authors A. Al Sibahee, Mustafa Ahmed, Salman Alhartomi, Mohammed A. Hasan, Ola A. Hirsi, Abdinasir Qasim, Hamza H. S. Ali, Ramzy Salh, Adeb T. Rashid, Abdulmuttalib