Keystroke dynamics multi-factor authentication system for selected Makerere University information systems

Abstract

This report presents the successful development of a Keystroke Dynamics System (KSDS) with the seamless integration of an Application Programming Interface (API) into selected Makerere University systems i.e. Academic Management Information System (ACMIS), Makerere University e-Human Resource Management System(e-HRMS) and Makerere University E-Learning Environment (MUELE). The primary objective of this project was to enhance security measures and improve user authentication processes by implementing pattern capture based on individual keystroke dynamics. The system leverages advanced Machine Learning (ML) algorithms to analyze typing patterns and identify distinctive biometric traits of users, thus establishing a robust and reliable authentication framework. The user experience was a central consideration throughout the development process. The Graphical User Interface (GUI) was carefully crafted to be intuitive and user-friendly, allowing users to effortlessly enroll and manage their keystroke dynamics profiles. The final system was thoroughly tested using real-world datasets from our very own colleagues to validate its effectiveness and accuracy. The results of the testing phase revealed remarkable performance. Chapter one focuses on the background of our study, the problems to be addressed, our major objectives, scope and the significance of our research Chapter two focuses on the review of other studies on the keystroke authentication system and other its comparison to other authentication methods Chapter three consists of our research methodology, requirements gathering techniques like questionnaires we used to collect information, data collection methods and a description of the development lifecycle stages Chapter four consists of a description of the designed system and its components, analysis of the information we gathered, user requirements among other components Chapter five consists of data outputs, programming languages used, tools and the testing phase Finally, chapter 6 concludes this report with discussions, limitations and recommendations for future works.