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Case Study : Deep Learning-Based Keystroke Logging for Enhanced User Identification in Banks

Keystroke Authentication to provide additional security

Client Background Our client is a product company that provides security solutions to banks. They were interested in implementing additional security measures beyond standard password-based authentication, and wanted a solution that could supplement user identification to prevent fraudulent activity.

Problem Statement The client sought to conceptualize and design a keyword logger that could be used to supplement user identification for banks.

Solution Approach Our team designed a deep learning-based algorithm that would analyze the typing patterns and behavior of each user to create a unique profile based on factors such as typing speed, error rate, and other characteristics that differentiate the user from others. The system would compare the profile of the current user to their stored profile. If there is a match, the user is granted access. If there is no match or if there is suspicion of fraudulent activity, the system would trigger additional security measures such as mobile OTP-based login.

To implement this solution, we designed a keyword logging-based solution that would send the timestamp for each keystroke along with the character typed. The system analyzed this data to create a unique profile for each user. The profiles were updated regularly to account for changes in the user's typing patterns.

Results Our solution provided enhanced security for banks and other financial institutions by supplementing user identification beyond traditional password-based authentication. By analyzing user behavior, the system was able to prevent fraudulent activity and provide additional security measures when necessary.

Conclusion Our deep learning-based keystroke logging solution is a powerful tool for enhancing user identification in banks and other financial institutions. By analyzing user behavior, our system provides a unique and effective approach to preventing fraudulent activity and securing sensitive information. We are proud to offer this solution to our clients and look forward to continuing to innovate in the field of predictive analytics.

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