<p>Within cloud computing environments, behavioral attacks on cloud networks target the behaviors and actions of users. Invaders can acquire unauthorized access, interface with the services, steal confidential data, or threaten system integrity by taking advantage of shortcomings in the cloud network infrastructure, protocols, or applications. It is essential to find behavioral assaults on cloud networks because the growing number of organizations depends on cloud services for data processing and storage. To strengthen the findings of behavioral assaults in cloud network environments, an ensemble model has been developed and shown in this study. The proposed model merges various features with progressive Machine learning approaches to upgrade detection accuracy. We indicate the efficacy of our proposed model in detecting various behavioural attacks in cloud computing networks by testing and assessing, and therefore present an improved defence system against cyber threats. The proposed model provides security in the cloud and an enhanced version to strengthen defences against all kinds of attacks. The study presents an advanced ensemble method joining multiple features with cutting-edge machine-learning techniques to enhance the detection of behavioral attacks in cloud networks. By evaluating its effectiveness against various attack types, the model demonstrates significant improvements in detection accuracy, providing a robust solution for evolving cyber threats in cloud security.</p>