In smart environments, comfort and efficiency are important goals in terms of the quality of human life. Recent developments in Internet of Things (IoT) technology have made it possible to design smart environments. IoT-based smart environments are concerned with security and privacy as key issues. Systems based on the IoT pose a security threat to smart environments. In order to prevent IoT-related security attacks which take advantage of some of these security vulnerabilities, intrusion detection systems (IDSs) designed for IoT environments are crucial. Data generated by connected objects in the age of the IoT provides the basis for big data analytics, which could be employed to identify patterns and identifies anomalies in data. In order to detect intrusions, most cyber security systems employ IDSs, which are used by a variety of techniques and architectures. As opposed to signature-based IDS, anomaly-based IDS learns the normal pattern of system behavior and alerts on abnormal events that occur, as opposed to monitoring monitored events against a database of known intrusion experiences. This paper focuses on the IDS implementation on the IoT network. The use of sensor devices to collect data from smart grid environments has led to smart grids becoming the preferred intrusion target due to the IoTs using advanced information technology. Clouds are data storage systems that provide a variety of smart infrastructure services, such as smart homes and smart buildings, over the internet. A deep learning-based intrusion detection system for the Internet of Things requires consideration of key design principles presented in this paper.