Cybersecurity has become a critical concern for individuals, organizations, and governments, especially with the rise of sophisticated cyberattacks and remote work environments. Traditional security approaches are no longer sufficient, leading to the adoption of advanced frameworks such as the zero-trust model, which operates on the principle “never trust, always verify.” This model enforces strict access controls and continuous monitoring across all network activities. Designing an intelligent zero-trust system is challenging due to the complexity of network environments and the evolving nature of malicious threats. This project proposes an advanced zero-trust architecture that integrates machine learning and multi-factor authentication (MFA) to strengthen security. Specifically, it employs Multilayer Perceptron models and k-Nearest Neighbors algorithms to analyze system logs and user behavior, enabling real-time anomaly detection and adaptive authentication mechanisms. The proposed framework is experimentally evaluated using the H-MOG behavioral–contextual authentication dataset, which captures multimodal user interaction patterns and supports continuous authentication analysis within Zero Trust environments. The integration of machine learning enhances the system’s ability to identify suspicious activities quickly and accurately, while MFA provides an additional layer of protection against unauthorized access. Moreover, the proposed framework emphasizes usability, ensuring that enhanced security does not impose excessive burden on users or IT teams. This allows the framework to respond more effectively to potential threats while maintaining usability. Overall, the proposed approach offers a practical and scalable solution that improves detection performance and strengthens continuous authentication and adaptive access control within Zero Trust environments.
Alshehri et al. (2026) studied this question.