An AI-Driven VM Threat Prediction Model for Multi-Risks Analysis-Based Cloud Cybersecurity

TL;DR

This paper introduces a novel machine learning-based VM threat prediction model that analyzes multiple risk factors to proactively identify cybersecurity threats in cloud environments, significantly reducing risks with minimal computational overhead.

Contribution

It presents a new multi-risk analysis model (MR-TPM) that effectively predicts VM threats by integrating various risk factors and user behavior, outperforming existing methods.

Findings

Reduces cybersecurity threats by up to 88.9%.

Efficiently predicts VM threats using historical and live data.

Outperforms existing threat detection approaches.

Abstract

Cloud virtualization technology, ingrained with physical resource sharing, prompts cybersecurity threats on users' virtual machines (VM)s due to the presence of inevitable vulnerabilities on the offsite servers. Contrary to the existing works which concentrated on reducing resource sharing and encryption and decryption of data before transfer for improving cybersecurity which raises computational cost overhead, the proposed model operates diversely for efficiently serving the same purpose. This paper proposes a novel Multiple Risks Analysis based VM Threat Prediction Model (MR-TPM) to secure computational data and minimize adversary breaches by proactively estimating the VMs threats. It considers multiple cybersecurity risk factors associated with the configuration and management of VMs, along with analysis of users' behaviour. All these threat factors are quantified for the generation of respective risk score values and fed as input into a machine learning based classifier to estimate the probability of threat for each VM. The performance of MR-TPM is evaluated using benchmark Google Cluster and OpenNebula VM threat traces. The experimental results demonstrate that the proposed model efficiently computes the cybersecurity risks and learns the VM threat patterns from historical and live data samples. The deployment of MR-TPM with existing VM allocation policies reduces cybersecurity threats up to 88.9%.