Federated Trust-Based Authentication for Secure Mobile Cloud Access

The proliferation of mobile cloud services significantly increases the complexity and risks associated with user authentication. Traditional password-based authentication methods are vulnerable to credential theft and account takeover attacks. Although centralized Risk-Based Authentication (RBA) methods enhance security by evaluating login attempt risks, they often compromise user privacy by aggregating sensitive authentication data. To overcome these challenges, this paper proposes a federated trust-based authentication framework utilizing federated learning (FL) integrated with an Artificial Neural Network enhanced by Batch Normalization (ANN-BN). Specifically, our framework calculates a trust score for each login attempt based on user behavior patterns and contextual threat indicators. This trust-based approach enables accurate detection of malicious login attempts while preserving user privacy by performing decentralized model training across multiple client devices. Experiments conducted on a real-world login dataset demonstrate that the proposed federated ANN-BN approach achieves high detection accuracy with a low false-alarm rate, effectively balancing security enhancement and privacy preservation. Our results confirm the effectiveness and practicality of federated learning for secure authentication in mobile cloud environments, highlighting its potential for real-world deployment and motivating future research directions.