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All Journal Journal of Robotics and Control (JRC)
Ghaleb, Fuad A
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Adaptive Intrusion Detection System with Ensemble Classifiers for Handling Imbalanced Datasets and Dynamic Network Traffic Almania, Moaad; Zainal, Anazida; Ghaleb, Fuad A; Alnawasrah, Ahmad; Al Qerom, Mahmoud
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.23648

Abstract

Intrusion Detection Systems (IDS) are crucial for network security, but their effectiveness often diminishes in dynamic environments due to outdated models and imbalanced datasets. This paper presents a novel Adaptive Intrusion Detection System (AIDS) that addresses these challenges by incorporating ensemble classifiers and dynamic retraining. The AIDS model integrates K-Nearest Neighbors (KNN), Fuzzy c-means clustering, and weight mapping to improve detection accuracy and adaptability to evolving network traffic. The system dynamically updates its reference model based on the severity of changes in network traffic, enabling more accurate and timely detection of cyber threats. To mitigate the effects of imbalanced datasets, ensemble classifiers, including Decision Tree (DT) and Random Forest (RF), are employed, resulting in significant performance improvements. Experimental results show that the proposed model achieves an overall accuracy of 97.7% and a false alarm rate (FAR) of 2.0%, outperforming traditional IDS models. Additionally, the study explores the impact of various retraining thresholds and demonstrates the model's robustness in handling both common and rare attack types. A comparative analysis with existing IDS models highlights the advantages of the AIDS model, particularly in dynamic and imbalanced network environments. The findings suggest that the AIDS model offers a promising solution for real-time IDS applications, with potential for further enhancements in scalability and computational efficiency.
Two-Level Feature Selection for Enhanced Accuracy and Reduced Computational Overhead in Intrusion Detection Systems Using Rough Set Theory and Binary Particle Swarm Optimization Almania, Moaad; Zainal, Anazida; Ghaleb, Fuad A; Alnawasrah, Ahmad; Al Qerom, Mahmoud
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Intrusion Detection Systems (IDS) are essential for safeguarding network infrastructures by detecting and mitigating malicious activities. This study introduces a two-level feature selection approach (TLFSA) designed to enhance classification accuracy and reduce computational overhead. The first phase employs Rough Set Theory (RST) to filter out irrelevant features, while the second phase uses Binary Particle Swarm Optimization (BPSO) to refine the feature subset based on their discriminative power. Experiments conducted on the NSL-KDD dataset show that the TLFSA approach outperforms traditional algorithms such as Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA), achieving a notable improvement of 0.99% in classification accuracy. Furthermore, class-specific feature subsets produced by the method demonstrate superior detection rates across all network traffic classes, with an average accuracy of 97.22%, compared to 91.11% for alternative methods. The proposed method effectively reduces the feature set to approximately 15% of the original features, streamlining the IDS model and improving both operational efficiency and real-time applicability.
Co-Authors Al Qerom, Mahmoud Almania, Moaad Alnawasrah, Ahmad Zainal, Anazida