An Improved and Intuitive Ensemble Model to Enhance Cloud Network
Behavioral Attack Detection
Abstract
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.
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