FedMUP: Federated Learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments

TL;DR

FedMUP is a federated learning-based model that predicts malicious users in cloud environments by analyzing user behavior and sharing model updates, significantly improving prediction accuracy and security.

Contribution

The paper introduces a novel federated learning approach for proactive malicious user prediction in cloud data security, enhancing accuracy without sharing raw data.

Findings

Improved malicious user prediction accuracy by up to 14.32%

Enhanced precision, recall, and F1-score significantly

Demonstrated efficiency over state-of-the-art methods

Abstract

Cloud computing is flourishing at a rapid pace. Significant consequences related to data security appear as a malicious user may get unauthorized access to sensitive data which may be misused, further. This raises an alarm-ringing situation to tackle the crucial issue related to data security and proactive malicious user prediction. This article proposes a Federated learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments (FedMUP). This approach firstly analyses user behavior to acquire multiple security risk parameters. Afterward, it employs the federated learning-driven malicious user prediction approach to reveal doubtful users, proactively. FedMUP trains the local model on their local dataset and transfers computed values rather than actual raw data to obtain an updated global model based on averaging various local versions. This updated model is shared repeatedly at regular intervals with the user for retraining to acquire a better, and more efficient model capable of predicting malicious users more precisely. Extensive experimental work and comparison of the proposed model with state-of-the-art approaches demonstrate the efficiency of the proposed work. Significant improvement is observed in the key performance indicators such as malicious user prediction accuracy, precision, recall, and f1-score up to 14.32%, 17.88%, 14.32%, and 18.35%, respectively.