Mobile Keystroke Biometrics Using Transformers

Stragapede, Giuseppe; Delgado-Santos, Paula; Tolosana, Rubén; Vera-Rodríguez, Rubén; Guest, Richard; Morales, Aythami

doi:10.1109/fg57933.2023.10042710

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…To create the framework, we consider two of the largest public databases of keystroke dynamics up to date, the Aalto Desktop [25] and Mobile [26] Keystroke Databases, extracting datasets that guarantee a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and that avoid too unbalanced subject distributions with respect to the considered demographic attributes. • We illustrate the main aspects of the proposed framework by considering two recent state-of-the-art keystroke biometric systems, TypeNet [11], and Type-Former [16], [43]. To this end, we propose a thorough analysis considering four different sets of features (Sec.…”

Section: Contributionsmentioning

confidence: 99%

“…TypeNet is implemented in Tensorflow [51]. • TypeFormer (2023) [16], [43]: a novel transformer architecture consisting in a temporal and a channel module enclosing two LSTM RNN layers, a Gaussian Range Encoding (GRE), a multi-head self-attention mechanism, and a block-recurrent transformer structure. TypeFormer is also trained with triplet loss.…”

Section: Biometric Verification Systemsmentioning

confidence: 99%

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Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation

Stragapede,

Vera-Rodriguez,

Tolosana

et al. 2024

IEEE Access

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Analyzing keystroke dynamics (KD) for biometric verification has several advantages: it is among the most discriminative behavioral traits; keyboards are among the most common human-computer interfaces, being the primary means for users to enter textual data; its acquisition does not require additional hardware, and its processing is relatively lightweight; and it allows for transparently recognizing subjects. However, the heterogeneity of experimental protocols and metrics, and the limited size of the databases adopted in the literature impede direct comparisons between different systems, thus representing an obstacle in the advancement of keystroke biometrics. To alleviate this aspect, we present a new experimental framework to benchmark KD-based biometric verification performance and fairness based on tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards, extracted from the Aalto Keystroke Databases. The framework runs on CodaLab in the form of the Keystroke Verification Challenge (KVC) a . Moreover, we also introduce a novel fairness metric, the Skewed Impostor Ratio (SIR), to capture inter-and intra-demographic group bias patterns in the verification scores. We demonstrate the usefulness of the proposed framework by employing two state-of-the-art keystroke verification systems, TypeNet and TypeFormer, to compare different sets of input features, achieving a less privacy-invasive system, by discarding the analysis of text content (ASCII codes of the keys pressed) in favor of extended features in the time domain. Our experiments show that this approach allows to maintain satisfactory performance.

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Section: Contributionsmentioning

confidence: 99%

Section: Biometric Verification Systemsmentioning

confidence: 99%

Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation

Stragapede,

Vera-Rodriguez,

Tolosana

et al. 2024

IEEE Access

Self Cite

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“…While, Acien et al [1] developed TypeNet, a Siamese Long-Short-Term-Memory (LSTM) model for large-scale keystroke authentication in free-text scenarios. Additionally, Stragapede et al [25] further advanced the field of keystroke authentication by incorporating a Transformer architecture and leveraging the power of Gaussian range encoding.…”

Section: Related Workmentioning

confidence: 99%

Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functions

Momeni,

babaali

2024

Preprint

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Keystroke biometrics is a promising approach for user identification and verification, leveraging the unique patterns in individuals’ typing behavior. In this paper, we propose a Transformer-based network that employs self-attention to extract informative features from keystroke sequences, surpassing the performance of traditional Recurrent Neural Networks. We explore two distinct architectures, namely bi-encoder and cross-encoder, and compare their effectiveness in keystroke authentication. Furthermore, we investigate different loss functions, including triplet, batch-all triplet, and WDCL loss, along with various distance metrics such as Euclidean, Manhattan, and cosine distances. These experiments allow us to optimize the training process and enhance the performance of our model. To evaluate our proposed model, we employ the Aalto desktop keystroke dataset. The results demonstrate that the bi-encoder architecture with batch-all triplet loss and cosine distance achieves the best performance, yielding an exceptional Equal Error Rate of 0.0186%. Furthermore, alternative algorithms for calculating similarity scores are explored to enhance accuracy. Notably, the utilization of a one-class Support Vector Machine reduces the Equal Error Rate to an impressive 0.0163%. The outcomes of this study indicate that our model surpasses the previous state-of-the-art in free-text keystroke authentication. These findings contribute to advancing the field of keystroke authentication and offer practical implications for secure user verification systems.

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“…Other methods [55][56][57][58] used non-RNN deep learning architectures. In [55], a CNN algorithm with transformer architecture was used for classification.…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Kokal

Vanamala

Dave

2023

JCP

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Throughout the past several decades, mobile devices have evolved in capability and popularity at growing rates while improvement in security has fallen behind. As smartphones now hold mass quantities of sensitive information from millions of people around the world, addressing this gap in security is crucial. Recently, researchers have experimented with behavioral and physiological biometrics-based authentication to improve mobile device security. Continuing the previous work in this field, this study identifies popular dynamics in behavioral and physiological smartphone authentication and aims to provide a comprehensive review of their performance with various deep learning and machine learning algorithms. We found that utilizing hybrid schemes with deep learning features and deep learning/machine learning classification can improve authentication performance. Throughout this paper, the benefits, limitations, and recommendations for future work will be discussed.

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Mobile Keystroke Biometrics Using Transformers

Cited by 15 publications

References 18 publications

Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation

Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation

Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functions

Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

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