A Machine Learning-Enabled Cloud Security Framework Integrating Biometric Authentication, Data Encryption, and Malware Detection — ISAISS 2026 | TechShield Publications
ISAISS 2026 · Conference Article

A Machine Learning-Enabled Cloud Security Framework Integrating Biometric Authentication, Data Encryption, and Malware Detection

Authors: Muhammad Abdullah Bajwa, Mina Fatima Malik, Sufyan Munawar, Farhan Hassan Malik

Abstract

Cloud computing has become a main program to store a large amount of data. However, unauthorized access and the uploading of files that contain malware are still major challenges of security. This paper presents an ML-based cloud system that uses biometric authentication and malware detection to secure data that is stored in the cloud. This system accomplishes multi-factor authentication by using face recognition and one-time password (OTP) that will be sent to the given email. After getting success in login, the user’s file will be passed through a malware detection process. The upload will be blocked and the file returned back to the user if any type of malware or malicious data is detected from the file; otherwise, the file will be uploaded safely to the cloud. This approach improves confidentiality and integrity by using strong identity verification and stopping malware from entering the cloud environment.

Cloud Security Biometric Authentication Multi-Factor Authentication Malware Detection Machine Learning

Cite This Paper

M. A. Bajwa, M. F. Malik, S. Munawar, and F. H. Malik, “A Machine Learning-Enabled Cloud Security Framework Integrating Biometric Authentication, Data Encryption, and Malware Detection,” Proc. Int. Symp. on AI and Secure Systems (ISAISS 2026), University of Central Punjab, Bahawalpur, Jan. 2026, doi: 10.67535/tsp.000002.033.