As per the latest data available from Statista, the worldwide number of IoT-connected devices is approximately 15,138.2 million. The ever-expanding array of devices within the IoT ecosystem presents new security challenges. Fraudsters may exploit weaknesses in these devices and networks to steal data, manipulate systems, or disrupt operations.One of the reasons behind the accelerated fraud within the IoT environment is online money transactions. Highly accurate fraud detection processes must be incorporated within the connected IoT world to mitigate the risk of various transactions and lessen the significant losses that may occur. Fortunately, cutting-edge technologies such as machine learning and deep learning provide robust solutions for addressing this challenge. Preventive measures to fight against IoT fraud can be made possible by thoroughly analyzing the network and realizing it by scrutinizing the network traffic to and from the IoT network to detect irregular patterns and behaviors. A robust security architecture is essential to maintain integrity and security of IoT environment.