MALICIOUS USER PREDICTION IN MULTI-TENANT CLOUDS USING FEDERATED LEARNING

Abstract

Malicious user activities in multi-tenant cloud environments pose significant security challenges due to the shared infrastructure and dynamic nature of cloud resources. Traditional centralized security mechanisms often fail to provide effective detection while preserving user privacy. This project proposes a Malicious User Prediction System in Multi-Tenant Clouds using Federated Learning (FL), which enables collaborative model training without sharing raw data among tenants.