Botnet is the type of malware that affects the target by infecting the device with specially designed malware to perform coordinated attacks or malicious activities like Distributed Denial of Service attacks (DDoS), data theft, spam distribution, etc.. These disruptions create a pressing need for the development of effective Internet of Things (IoT) Botnet attack detection methods in the industry and research community as well. However most of the existing techniques are used to identify the IoT Botnet attack by using Machine Learning (ML) and Deep Learning (DL) approaches which is a time consuming approach. In this context, this paper presents an innovative Finite State Machine (FSM) based methods for IoT botnet attack detection. It provides a structured approach to recognize and differentiate between normal and IoT network attack. The experimental results indicate that the designed method efficaciously detects IoT Botnet attack variants with improved accuracy.