Design and Implementation of a Cloud-Based Secure File Management System Using Facial Recognition and AES Encryption on AWS EC2

Abstract

The high rate of cloud computing has created the need to develop effective security systems that will ensure the safety of sensitive information in distributed systems. This research paper introduces an elaborate cloud-based file management system that incorporates the use of the facial recognition technology with advanced cryptographical methods in order to protect the secure storage and accessibility of data. The suggested architecture uses AWS EC2 as a scalable cloud storage service and MongoDB as an efficient data storage and retrieval tool to provide a smooth and safe file storage and retrieval space. The system provides end-to-end protection of user data, which is ensured by the AES-256-GCM encryption, and a convenient, though secure, authentication system is offered by the facial recognition component. The performance analysis proves that the system is highly efficient with an average encryption of 47ms and a decryption time of 31ms to encrypt and decrypt 1MB files, respectively, and can therefore be effectively implemented in the real world in any security sensitive environment. The architecture will be scalable, fault tolerant, and with the ability to support multiple simultaneous users and have a high level of security measures.

Introduction

With the rapid growth of digital data and online threats, traditional password-based authentication systems have become vulnerable to attacks such as brute-force attacks, phishing, and credential stuffing. To overcome these limitations, this study proposes a secure cloud-based file management system that integrates facial recognition technology with strong encryption to provide both high security and a user-friendly authentication process.

The system uses facial recognition as a biometric authentication method combined with AES-256-GCM encryption to protect sensitive files. Cloud infrastructure such as AWS EC2 provides scalability and cost-effective computing resources, while MongoDB Atlas ensures reliable data storage and retrieval. The modular architecture allows the system to be easily expanded or customized for different organizational needs.

The methodology follows an Agile development approach involving design, implementation, testing, and evaluation. The facial recognition module uses computer vision and machine learning algorithms trained on diverse facial images to generate a unique biometric template for each user. The encryption module ensures data confidentiality and integrity, while the microservices architecture allows different system components to scale independently.

In the implementation, the backend is developed using Python, and the frontend uses React.js for a responsive user interface. During user registration, multiple facial images are captured and converted into biometric templates stored securely in the database. Files uploaded by users are encrypted with unique keys, which are further protected by a master key. The system also includes logging and auditing features to track security events and support forensic analysis.

The security analysis involved penetration testing and vulnerability assessment. The facial recognition system achieved a false acceptance rate below 0.1% and false rejection rate below 2%, showing strong resistance to spoofing attacks such as photos, videos, and 3D masks. Additional protections such as secure tokens, short-lived sessions, rate limiting, account lockouts, and secure communication protocols were implemented to prevent attacks like session hijacking, replay attacks, SQL injection, XSS, and CSRF.

Comparative analysis shows that the proposed system provides stronger security than traditional cloud storage services. It offers AES-256 client-side encryption, secure processing, and full metadata protection, reducing vulnerabilities significantly.

The results demonstrate strong performance. The facial recognition system achieved 98.7% accuracy, and encryption operations were fast, with 47 ms for encryption and 31 ms for decryption of a 1 MB file. The system could handle up to 10,000 simultaneous users with response times under 2 seconds. User testing showed 94% satisfaction, confirming that the system is both secure and easy to use.

Conclusion

This paper provided an instance of the effective combination of facial recognition technology with 256 encryption in a secured file management service in the cloud. The solution suggested manages to counter the flaws of the conventional password based authentication model through creation of powerful, user friendly, and resistant to manipulation access control system. It was established through performance tests that the system is very efficient, scalable and can run reliably under heavy user loads hence can be used in both small and large organizations with high security standards. The modular approach is designed to be flexible and maintainable over a long period of time to enable the system to change with new cybersecurity requirements and cloud-based technologies. Altogether, the study is a solid foundation on how one can safely store the data in the cloud and preconditions further improvement, including multi-cloud architectures, key vaults supported by hardware, and biometric authentication.

References

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Copyright

Copyright © 2026 Dr. Tadi. Chandrasekhar, Prof. Th. Basanta, Dr. Mutum.Bidyarani Devi, Dr. J. N. Swaminathan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.