Bansal, P.; Ouda, A. Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics. Computers 2024, 13, 103, doi:10.3390/computers13040103.
Bansal, P.; Ouda, A. Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics. Computers 2024, 13, 103, doi:10.3390/computers13040103.
Bansal, P.; Ouda, A. Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics. Computers 2024, 13, 103, doi:10.3390/computers13040103.
Bansal, P.; Ouda, A. Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics. Computers 2024, 13, 103, doi:10.3390/computers13040103.
Abstract
This article focuses on developing a continuous authentication system using behavioral biometrics to recognize users accessing computing devices. The user’s distinct behavioral biometric is captured through keystroke dynamics, and reward-based reinforcement learning (RL) ideas are applied to recognize them throughout the session. The suggested system adds an extra layer of security to traditional authentication methods, forming a robust continuous authentication system that can be added to static authentication systems. The methodology involves training a RL model to detect unusual user typing patterns and flag suspicious activity. Each user has an agent trained on their historical data, which is preprocessed and used to create episodes for the agent to learn from. The environment involves fetching observations and randomly corrupting them to learn out-of-order behavior. The observation vector includes both running features and summary features. The re-ward function is binary and minimalistic. The Principal Component Analysis (PCA) model is used to encode the running features, and the Double Deep Q-Network (DDQN) algorithm with a fully connected neural network is used as the policy net. The evaluation achieved an average training accuracy and EER (equal error rate) of 94.7% and 0.0126 and test accuracy and ERR of 81.06% and 0.0323 for all users when the number of encoder features was increased. Therefore, it is concluded that by continuously learning and adapting to changing behavior patterns, this approach can provide more secure and personalized authentication, lowering the possibility of unauthorized access and cyberattacks. Overall, the use of reinforcement learning and behavioral biometrics for continuous authentication has the potential to significantly enhance security in the digital age and are effective in identifying each user.
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
