Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and remotely accessible smart devices, more and more cybersecurity issues are being witnessed among cyber-physical systems. A perfect intrusion detection system (IDS) can probably identify various cybersecurity issues and their sources. In this article, using various telemetry datasets of different Internet of Things scenarios, we exhibit that external users can access the IoT devices and infer the victim user’s activity by sniffing the network traffic. Further, the article presents the performance of various bagging and boosting ensemble decision tree techniques of machine learning in the design of an efficient IDS. Most of the previous IDSs just focused on good accuracy and ignored the execution speed that must be improved to optimize the performance of an ID model. Most of the earlier pieces of research focused on binary classification. This study attempts to evaluate the performance of various ensemble machine learning multiclass classification algorithms by deploying on openly available “TON-IoT” datasets of IoT and Industrial IoT (IIoT) sensors.

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Machine learning (ML) provides effective solutions to develop efficient intrusion detection system (IDS) for various environments. In the present paper, a diversified study of various ensemble machine learning (ML) algorithms has been carried out to propose design of an effective and time-efficient IDS for Internet of Things (IoT) enabled environment. In this paper, data captured from network traffic and real-time sensors of the IoT-enabled smart environment has been analyzed to classify and predict various types of network attacks. The performance of Logistic Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine classifiers have been benchmarked using an open-source largely imbalanced dataset 'DS2OS' that consists of 'normal' and 'anomalous' network traffic. An intrusion detection model ''LGB-IDS'' has been proposed using the LGBM library of ML after validating its superiority over other algorithms using ensemble techniques and on the basis of majority voting. The performance of the proposed intrusion detection system is suitably validated using certain performance metrics of machine learning such as train and test accuracy, time efficiency, error-rate, true-positive rate (TPR), and false-negative rate (FNR). The experimental results reveal that XGB and LGBM have almost equal accuracy, but the time efficiency of LGBM is much better than RF, and XGB classifiers. The main objective of the present paper is to propose a design of an efficient intrusion detection model with high accuracy, better time efficiency, and reduced false alarm rate. The experimental results show that the proposed model achieves an accuracy of 99.92% and the time efficiency comes to be much higher than other prevalent algorithms-based models. The threat detection rate is greater than 90% and less than 100%. Time complexity of LGBM is also very much low as compared to other ML algorithms.INDEX TERMS Machine learning classification algorithms, ensemble classifiers, gradient boosting algorithms, light gradient boosting machines (LGBM), and intrusion detection systems (IDS).Open Access funding provided by 'Università degli Studi di Enna "KORE"' within the CRUI CARE Agreement

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