Comparison of AWS and AZURE in Prototype for Automated Selected Security Controls

Abstract

The cloud computing phenomenon has achieved global popularity, with enterprises increasingly relying on cloud services for day-to-day business operations. However, the rapid dissemination of new malicious code variants with zero-day assaults in the cloud creates confusion and broad worry because the attackers' motives often remain unknown. This paper discusses a safer computing platform or model that detects harmful or malicious code in a cloud environment and automatically selects the best security control for defence. Automated selection of the best security controls for real-time defence is crucial in cloud environments. The study utilizes pefile library in Python to extract signature bytes, N-gram algorithm for signature bytes segmentation, the C4.5 algorithm for constructing signature clusters, and a Python program to determine the best security control. The model was developed and tested using Microsoft Azure and the Amazon Web Services cloud infrastructure, with results demonstrating its effectiveness on both platforms in detecting malicious code and timely selecting an optimal security control for real-time defence.