BioPrivacy: Development of a Keystroke Dynamics Continuous Authentication System

Stylios, Ioannis; Skalkos, Andreas; Kokolakis, Spyros; Karyda, Maria

doi:10.1007/978-3-030-95484-0_10

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…39 users participated in data collection which consisted of either memorizing a sentence or number sequence and inputting it or inputting the sentence or number sequence immediately. During testing, the MLP algorithm was able to achieve an accuracy of 97.18% and an EER of 0.02%, (Stylios et al, 2022) which greatly outperformed the performance of MLP in other related works. However, this model did not test against any known attacks, which could influence the effectiveness of the schema.…”

Section: Related Wo Rksmentioning

confidence: 76%

“…However, the use of a small dataset may affect the actual effectiveness of this schema. Stylios et al (2022) propose a schema for continuous authentication using keystroke dynamics and a new data collection tool. Muti-level Perceptron is the only algorithm being used in the training and testing of this model, in an attempt to see if the use of the new collection tool increases the performance of MLP.…”

Section: Related Wo Rksmentioning

confidence: 99%

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Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

Pryor¹,

Mallet²,

Dave³

et al. 2022

CIS

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The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulnerabilities of current authentication methods such as physiological biometrics or passwords. Instead of these methods, behavioral biometrics has recently been researched as a solution to these vulnerable authentication methods. In this study, we aim to contribute to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement, and we evaluate the performance of different single-modal and multi-modal combinations of the two biometrics. Using two publicly available datasets - BioIdent and Hand Movement Orientation and Grasp (H-MOG), this study uses seven common machine learning algorithms to evaluate performance. The algorithms used in the evaluation include Random Forest, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Logistic Regression, Multilayer Perceptron, and Long Short-Term Memory Recurrent Neural Networks, with accuracy rates reaching as high as 86%.

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Section: Related Wo Rksmentioning

confidence: 76%

Section: Related Wo Rksmentioning

confidence: 99%

Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

Pryor¹,

Mallet²,

Dave³

et al. 2022

CIS

View full text Add to dashboard Cite

show abstract

“…Hence, it is computationally intensive and requires both genuine and imposter data to train. Neural network is capable of producing much better performance than the statistical algorithms and any other machine learning algorithms when working with big data that has both genuine and impostor samples [5,52,108,90].…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

Keystroke Dynamics: Concepts, Techniques, and Applications

Shadman¹,

Wahab²,

Manno³

et al. 2023

Preprint

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Reliably identifying and authenticating users remains integral to computer system security. Various novel authentication tenchniques such as biometric authentication systems have been devised in recent years. This paper surveys keystroke-based authentication systems and their applications such as continuous authentication. Keystroke dynamics promises to be non-intrusive and cost-effective as no addition hardware is required other than a keyboard. This survey can be a reference for researchers working on keystroke dynamics.

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“…Stylios, Ioannis, et al [15] collected keystroke actions for smartphones in Android applications. Create a feature using the duration, latency, pressure, X, Y coordinates of keys generated from the smartphone's virtual keyboard.…”

Section: Continuous Authentication Using Key Latencymentioning

confidence: 99%

PC-based User Continuous Authentication System Using the User's Finger Stroke Characteristics

Hyung-dong Lee,

KiHyo Nam,

Heewoong Lee

et al. 2023

ReBICTE

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Biometric technology, which performs continuous authentication based on user behavior, has been developed in various ways depending on the type of device, input device, and sensor. Research on continuous authentication technology in PC-based systems with few sensors installed is based on data from 3physical devices that extract and analyze features from keyboard and mouse input patterns. Among these, previous studies on continuous authentication through keyboard input performed continuous authentication using the key hold delay time that occurs when one key is pressed, the key interval delay time that occurs due to the interaction between fingers, and the key press delay time. did However, the keyboard-based continuous authentication model has limitations in increasing accuracy due to a small number of features. Therefore, in this paper, when a user inputs a sentence using a QWERTY keyboard in a PC system, the function is subdivided by reflecting the characteristics of each finger and used for continuous authentication. The features extracted by reflecting the characteristics of the finger were subdivided into a total of 151 latencies, and SVDD, decision tree, and CNN were used as continuous authentication models. Experimental data was collected through the user's input of randomly displayed sentences, and features were created based on this. User keystroke behavior was used to validate the continuous authentication model. Validation metrics included thresholds for classification accuracy (ACC), ROC curves, false rejection rate (FRR), equal error rate (EER), and false acceptance rate (FAR). As a result of the experiment, it was found that continuous authentication including the user's finger input pattern was superior to the existing method.

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BioPrivacy: Development of a Keystroke Dynamics Continuous Authentication System

Cited by 9 publications

References 33 publications

Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

Keystroke Dynamics: Concepts, Techniques, and Applications

PC-based User Continuous Authentication System Using the User's Finger Stroke Characteristics

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