Behavioral attacks on cloud networks target the behaviors and actions of users within cloud computing environments. Attackers can obtain unauthorized access, interfere with services, steal confidential information, or jeopardize system integrity by taking advantage of flaws in the cloud network’s infrastructure, protocols, or apps. Given the growing number of organizations depending on cloud services for data processing and storage, it is imperative to spot behavioral assaults on cloud networks. In order to improve the detection of behavioral assaults in cloud network environments, a unique ensemble technique has been developed and presented in this study. The suggested model combines multiple features with cutting-edge machine-learning approaches to improve detection accuracy. We exhibit the efficacy of our methodology in detecting diverse behavioral attacks in cloud networks through testing and assessment, hence providing improved defense against ever-changing cyber threats. Our work sets a new standard for cloud security and offers a workable way to strengthen defenses against advanced assaults. The paper introduces an advanced ensemble method combining 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.