An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

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.

Automated summary

Internet of Things (IoT) is a rapidly growing technology that connects billions of smart devices through the Internet, and it has applications in various domains such as healthcare and transportation. A secure network is essential for IoT, but with the increasing number of interconnected devices, cybersecurity issues are becoming more prevalent. This article demonstrates that external users can access IoT devices and infer user activity through network traffic analysis. The article also evaluates the performance of various machine learning algorithms in designing an efficient intrusion detection system (IDS) using telemetry datasets from different IoT scenarios.