A Hybrid Model for Minimizing Malware Threats in Cloud Computing Using Generative Adversarial Networks (GANs) and Federated Learning (FL)

Abstract

The objective of this study is to develop a hybrid model for minimizing malware threats in cloud computing model using Generative Adversary Networks (GANs) and Federated Learning (FL), to improve detection accuracy and reduce false positives using the hybrid model. The project was motivated by the prevailing challenges such as Model complexity, Data privacy and security, Malware detection in cloud computing, Data quality issues and Evaluation challenges. The methodology adopted is the Cross Industry Standard Process for Data Mining (CRISP- DM) hybrid methodology which involves structured data analysis, like fraud detection, customer segmentation. The programming language used for the implementation is python programming language. Expected results demonstrate significant improvement in detection accuracy, restore security in cloud computing practice ensuring confidence, integrity and privacy.