IoT Intrusion Detection System Based on Machine Learning

被引:3
|
作者
Xu, Bayi [1 ]
Sun, Lei [2 ]
Mao, Xiuqing [2 ]
Ding, Ruiyang [2 ]
Liu, Chengwei [3 ]
机构
[1] Zhengzhou Univ, Sch Cyber Sci & Engn, Zhengzhou 450000, Peoples R China
[2] Informat Engn Univ, Acad 3, Zhengzhou 450000, Peoples R China
[3] China Elect Technol Grp Corp, Res Inst 28, Nanjing 210007, Peoples R China
关键词
intrusion detection; feature selection; BGWO; XGBoost; FEATURE-SELECTION; INTERNET; OPTIMIZATION; THINGS;
D O I
10.3390/electronics12204289
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid development of the Internet of Things (IoT), the number of IoT devices is increasing dramatically, making it increasingly important to identify intrusions on these devices. Researchers are using machine learning techniques to design effective intrusion detection systems. In this study, we propose a novel intrusion detection system that efficiently detects network anomalous traffic. To reduce the feature dimensions of the data, we employ the binary grey wolf optimizer (BGWO) heuristic algorithm and recursive feature elimination (RFE) to select the most relevant feature subset for the target variable. The synthetic minority oversampling technique (SMOTE) is used to oversample the minority class and mitigate the impact of data imbalance on the classification results. The preprocessed data are then classified using XGBoost, and the hyperparameters of the model are optimized using Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to achieve the highest detection performance. To validate the effectiveness of the proposed method, we conduct binary and multiclass experiments on five commonly used IoT datasets. The results show that our proposed method outperforms state-of-the-art methods in four out of the five datasets. It is noteworthy that our proposed method achieves perfect accuracy, precision, recall, and an F1 score of 1.0 on the BoT-Iot and WUSTL-IIOT-2021 datasets, further validating the effectiveness of our approach.
引用
收藏
页数:21
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