Abstract
The idea of a smart city is to connect physical objects or things with sensors, software, electronics, and Internet connectivity for data communication through the Internet of Things (IoT) devices. IoT enhances productivity and efficacy intelligently using remote management, but the risk of security and privacy increases. Cyber threats are advancing day by day, causing insufficient measures of security and confidentiality. As the hackers use the Internet, several IoT vulnerabilities are introduced, demanding new security measures in the IoT devices of the smart city. The threads concerned with IoT need to be reduced for efficient Intrusion Detection Systems (IDSs). As a result, machine learning algorithms generate correct outputs from a large and complicated dataset. The output of machine learning could be used to detect anomalies in IoT-network systems. This paper employed several machine learning classifiers and a deep learning model for intrusion detection using seven datasets of the TON_IoT telemetry dataset. The proposed IDS achieved an accuracy of 99.7% using Thermostat, GPS Tracker, Garage Door, and Modbus datasets via voting classifier.