Advancing User Authentication in Cloud Computing: Automated Smart Application as a Multi-Factor Approach Beyond Extended-Levenshtein Distance

This research aims to unveil the area of user authentication in cloud computing. The study aims to investigate an algorithm known as the ‘Confidence Range’ (CR) algorithm, which was introduced in cloud computing to address its limitations and reduce discrepancies in password security. The CR algorithm creates individual EPSBs for password-authorized users, allowing password activity to be monitored and helping them remember their passwords. Additionally, password activity can be monitored to identify anomalies. However, as a result, this work identified a loophole in the CR algorithm, precisely when the same password is used in different positions. To address this vulnerability, this work introduces a new algorithm. The extended algorithm also shows promising results compared to the CR and Levenshtein Distance algorithms. However, the weaknesses of the Confidence Range algorithm are revealed in the second experiment: Its ability to distinguish between fake and genuine users is low, with a recognition precision rate of only 2.94%. Levenshtein Distance algorithm promises a precision of 19, on the other hand, 11%, and the precision of the Levenshtein Distance with the Confidence Range algorithm expanded is 27. 94%. These results suggest a need for continuous improvement and assessment of authentication security in cloud computing, where Confidence Range approaches offer potential enhancements to password protection systems. Further studies are needed to explore alternative strategies and to improve password protection systems in real-world contexts.