Adaptive mutual information ABC and Swish-TCN: Cutting-edge approaches for feature selection and cloud security

Abstract

Cloud virtual machines (VMs), which offer dynamic resource distribution and economical solutions, are extensively used for scalable and effective computing in a range of sectors. Cloud environments are getting more and more prevailing, but this has made them appealing prey for ransomware attacks, which can encrypt substantial data, interrupt services, and negatively impact functioning continuity. This study proposes a scheme for ransomware detection (RD) in cloud environments bedded on Swish-Activated Temporal Convolutional Networks (SA-TCNs) to prevail these difficulties. Utilising sophisticated temporal modelling with Swish-TCN and optimal feature selection with the Artificial Bee Colony (ABC) algorithm, the framework excels current methods like Pulse, SINN-RD, T-BECA, and BSFR-SH, achieving a high accuracy of 99.51%. The system enhances resources with low CPU and memory utilization per VM. It efficaciously identifies ransomware with rapid detection times, maintaining real time applicability and scalability across small to medium-scale cloud systems. This study provides a reliable and effectual way to mitigate ransomware risks in dynamic cloud systems while maintaining data security and service continuity.