Optimization Strategies for Cyber Threat Detection in Cloud Architectures Leveraging Deep Machine Learning for Advanced Malware Identification
Ankit R. Mune, Arpit U. Chaudhari, M. A. Pund, Tejas Prashantrao Adhau, Sumedh P. Ingale, Aditya O. Sable · 2024
The fast growth of cloud designs has created new security holes, which makes finding cyber threats a very important issue. This study looks into improvement methods that are meant to make it easier to find cyber threats in cloud settings. Using deep machine learning methods, the study aims to make it easier to spot advanced malware, which is very dangerous for cloud infrastructure. The recommended strategy combines optimization strategies with profound learning models to move forward the speed and precision of recognizable proof. The strategy incorporates looking at huge sets of information from cloud frameworks to educate models that can discover complex malware patterns. To discover out how well the moved forward discovery framework works, execution measures just like the rate of location, the rate of untrue positives, and the taken a toll of computing are looked at. The study comes about appear that profound machine learning is much superior at finding malware than standard strategies. This appears that profound machine learning has the capacity to create cloud security more grounded. This consider makes a difference make solid, versatile ways to discover cyber dangers in cloud frameworks. It gives us useful information for making security way better in a world that's becoming increasingly advanced.