Securing IoT: Anomaly Detection and Behavioral Analysis for Data Integrity

The Internet of Things (IoT) is a hub for many internet-connected devices, however IoT devices with limited resources have inherent security risks when sending data. This study presents a new paradigm that uses anomaly detection and behavioural analysis to improve security in cloud-based IoT communication. This method, in contrast to conventional cryptographic methods, concentrates on closely examining device behaviour to identify anomalous patterns, guaranteeing data integrity and confidentiality throughout transmission. In order to strengthen the security of data transfer inside IoT sensor networks, this research provides a unique method that uses machine learning andbehavioural analysis customized for IoT devices to identify anomalous behavior. By putting these approaches into practice, this system aims to greatly increase the resilience of IoT communication networks against possible attacks, creating a more secure framework for data transfer and exchange in the IoT environment.