Keystroke Biometric Data for Identity Verification: Performance Analysis of Machine Learning Algorithms

Abstract

Cyber-attacks are on the rise in today's environment, where traditional security measures are ineffective. As a result, the adoption of cutting-edge tools such as artificial intelligence technology is critical in the fight against cyber threats. User behaviors, such as keyboard dynamics, provide potential data that can be used for protection against cyber-attacks. Keystroke dynamics is one of the fastest and most cost-effective methods that can be used to detect user behaviors, as it can be captured using standard user keyboards. The analysis of this data and protection against cyber-attacks is made possible through machine learning algorithms. Based on keyboard dynamics, this study analyzes the performance of k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Neural Network (NN) methods for user behavior analysis and anomaly detection. The findings shed light on the significance of artificial intelligence in cyber security by examining the accomplishments of several machine learning algorithms. The study's findings may serve as a foundation for future research and novel solutions in the realm of cyber security.