Keystroke Dynamics Based Authentication System

Cevik, Nursah; Akleylek, Sedat; Koc, Kadir Yunus

doi:10.1109/ubmk52708.2021.9559008

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…The states in the MDP could represent different observations of the user's keystrokes, such as the timing between two subsequent key presses, the duration of time between of each key press, or the sequences of characters typed. The actions in the MDP could represent different authentication decisions, such as allowing access or denying access [27]. And the rewards in the MDP could represent the level of confidence in the authentication decision, with higher rewards assigned to more confident decisions and lower rewards assigned to less confident decisions.…”

Section: The Proposed Rl Frameworkmentioning

confidence: 99%

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Bansal,

Ouda

2024

Computers

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This research article delves into the development of a reinforcement learning (RL)-based continuous authentication system utilizing behavioral biometrics for user identification on computing devices. Keystroke dynamics are employed to capture unique behavioral biometric signatures, while a reward-driven RL model is deployed to authenticate users throughout their sessions. The proposed system augments conventional authentication mechanisms, fortifying them with an additional layer of security to create a robust continuous authentication framework compatible with static authentication systems. The methodology entails training an RL model to discern atypical user typing patterns and identify potentially suspicious activities. Each user’s historical data are utilized to train an agent, which undergoes preprocessing to generate episodes for learning purposes. The environment involves the retrieval of observations, which are intentionally perturbed to facilitate learning of nonlinear behaviors. The observation vector encompasses both ongoing and summarized features. A binary and minimalist reward function is employed, with principal component analysis (PCA) utilized for encoding ongoing features, and the double deep Q-network (DDQN) algorithm implemented through a fully connected neural network serving as the policy net. Evaluation results showcase training accuracy and equal error rate (EER) ranging from 94.7% to 100% and 0 to 0.0126, respectively, while test accuracy and EER fall within the range of approximately 81.06% to 93.5% and 0.0323 to 0.11, respectively, for all users as encoder features increase in number. These outcomes are achieved through RL’s iterative refinement of rewards via trial and error, leading to enhanced accuracy over time as more data are processed and incorporated into the system.

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Section: The Proposed Rl Frameworkmentioning

confidence: 99%

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Bansal,

Ouda

2024

Computers

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show abstract

“…The states in the MDP could represent different observations of the user's keystrokes, such as the timing between 2 subsequent key presses, the duration of time between of each key press, or the sequences of characters typed. The actions in the MDP could represent different authentication decisions, such as allowing access or denying access [27]. And the rewards in the MDP could represent the level of confidence in the authentication decision, with higher rewards assigned to more confident decisions and lower rewards assigned to less confident decisions.…”

Section: The Proposed Reinforcement Learning (Rl) Frameworkmentioning

confidence: 99%

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Bansal¹,

Ouda²

2023

Preprint

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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.

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Enhanced Keystroke Dynamics Authentication Using Keystroke Vector Dissimilarity

Bussabong,

Anusas-amornkul

2023

2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE)

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Keystroke Dynamics Based Authentication System

Cited by 5 publications

References 13 publications

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Enhanced Keystroke Dynamics Authentication Using Keystroke Vector Dissimilarity

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