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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE sensors journal Jg. 25; H. 1; S. 1768 - 1783
Hauptverfasser: Jiang, Laiwei, Gu, Haiyang, Xie, Lixia, Yang, Hongyu, Na, Zhenyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 optimization algorithm (IAOA)-extreme gradient boosting tree (XGBoost). A synthetic minority oversampling technique, Tomek Link (SMOTE-Tomek), is used to reduce the impact of data imbalance on detection performance. In the proposed IAOA, logistic-tent chaos mapping and opposition-based learning (LTOBL) is incorporated to generate an efficient and even distribution initial population during the optimization process. Nonlinear inertia weights and Gaussian distribution are introduced to enhance the global and local search capabilities. Lévy flight is incorporated to randomly perturb the optimal individuals, enhancing population diversity and preventing the search from falling into the local optimal solution. To verify the effectiveness of the proposed method, we use 11 benchmark test functions and make simulation experiments through the WSN-DS dataset and KDDCUP 99 dataset. Simulation results show that, compared with reference algorithms, the proposed IAOA performs well in the benchmark function test, the ST-IAOA-XGBoost achieved significant improvements in terms of Accuracy, Precision, Recall, and <inline-formula> <tex-math notation="LaTeX">{F}1 </tex-math></inline-formula> score on the WSN-DS dataset, with increases of up to 17%, 12%, 17%, and 27%, respectively. Similarly, on the KDDCUP 99 dataset, the enhancements were up to 22%, 23%, 22%, and 23% for Accuracy, Precision, Recall, and <inline-formula> <tex-math notation="LaTeX">{F}1 </tex-math></inline-formula> score, respectively.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3489623