ST-IAOA-XGBoost: An Efficient Data-Balanced Intrusion Detection Method for WSN

To address the limitations of traditional machine learning method in the detection of minortiy class samples and the overall detection accuracy in wireless sensor networks (WSNs) intrusion detection (ID), this article proposes an ID method for WSN-based SMOTE-Tomek (ST)-improved arithmetic optimizat...

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Vydané v:	IEEE sensors journal Ročník 25; číslo 1; s. 1768 - 1783
Hlavní autori:	Jiang, Laiwei, Gu, Haiyang, Xie, Lixia, Yang, Hongyu, Na, Zhenyu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Algorithms Benchmarks Classification algorithms Datasets Deep learning Extreme gradient boosting tree (XGBoost) Gaussian process improved arithmetic optimization algorithm (IAOA) Intrusion detection intrusion detection (ID) Intrusion detection systems Machine learning Machine learning algorithms Metaheuristics Normal distribution Optimization Recall Sensors Simulation SMOTE-Tomek (ST) Support vector machines wireless sensor network (WSN) Wireless sensor networks
ISSN:	1530-437X, 1558-1748
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