Managerial Implications for Adopting Advanced Biometric Authentication: A Case Study on Optimizing Keystroke Dynamics Using ISKKC

Abstract

This paper offers a technology management lens for deploying and optimising advanced biometric authentication systems, utilising the Integrated SVM-KNN Keystroke Classifier (ISKKC) model as a case study. The ISKKC system employs an innovative machine learning stack, including regression classifiers, k Nearest Neighbors (KNN), and Support Vector Machines (SVM), to amplify the accuracy of keystroke dynamics, especially in multi-lingual settings. Utilising a comprehensive research design, we gathered and analysed data from 60 participants who are native speakers of either Malay or Chinese. The study's findings substantiate that ISKKC surpasses standalone authentication models in various performance metrics such as accuracy, precision, recall, and specificity. For technology managers, these results translate into strategic advantages, including enhanced system reliability, lower risk of security breaches, and higher overall Return On Investment (ROI). The study also explores managerial considerations such as the ease of ISKKC's integration into existing technology infrastructures, the costs associated with its implementation, and the strategic alignment with organisational objectives. Furthermore, we discuss how the ISKKC model can contribute to a sustainable competitive advantage by offering superior authentication security, facilitating compliance with regulatory standards and customer trust in an increasingly digital ecosystem. This research aims to guide technology managers to make informed decisions on adopting and optimising biometric authentication systems, emphasising the operational and strategic advantages conferred by machine learning algorithms like those incorporated in ISKKC.