IoT Intrusion Detection System Based on Machine Learning

Abstract

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.

Hierarchical Classification of Botnet Using Lightweight CNN

“…Comparing our suggested methodology with current state-of-the-art methods allowed for a thorough assessment of its effectiveness, provided in Tables 4-6. In particular, our method was thoroughly tested against prominent techniques [14,17,18]. This thorough comparison study sought to clarify our suggested approach's unique benefits and functional characteristics with these state-of-the-art works.…”

“…Xu et al [17] employed the binary grey wolf optimizer (BGWO) heuristic algorithm and recursive feature elimination (RFE) to select features for their intrusion detection system. They used the synthetic minority oversampling technique (SMOTE) to oversample the minority classes.…”

“…Precision, recall, F1 score, and accuracy are metrics commonly used to evaluate the performance of classification models [17]. These metrics are calculated based on the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).…”

“…These metrics are calculated based on the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). These terms are commonly used in binary classification scenarios, where a model is predicting between two or more classes (positive and negative) [17].…”

Cyber Security Intrusion Detection and Bot Data Collection using Deep Learning in the IoT

“…Authors in [23], proposed a CorrAUC that uses a feature selection metric and an algorithm to filter features accurately. The procedure is assessed using the dataset of the Bot-IoT and four different machine learning methods, the average accuracy of which is over 96%.In [24], authors proposed a method of intrusion detection for IoT devices that utilizes machine learning to identify anomalous traffic in the network. The system uses binary grey wolf optimizer, recursive feature elimination, synthetic minority oversampling technique, XGBoost, Bayesian, and classification optimization with a tree-structured Parzen estimator.…”