Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach.
In: Sensors (14248220), Jg. 24 (2024-06-01), Heft 11, S. 3339-3364
Online
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Zugriff:
Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost's hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively. [ABSTRACT FROM AUTHOR]
Titel: |
Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach.
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Autor/in / Beteiligte Person: | Nguyen, Thuan Minh ; Vo, Hanh Hong-Phuc ; Yoo, Myungsik |
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Zeitschrift: | Sensors (14248220), Jg. 24 (2024-06-01), Heft 11, S. 3339-3364 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 1424-8220 (print) |
DOI: | 10.3390/s24113339 |
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