Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication

Kokal, Sara; Pryor, Laura; Dave, Rushit

doi:10.1145/3543712.3543732

Cited by 8 publications

(8 citation statements)

References 4 publications

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“…In previous reviews [5,8], it was established that to continue progress in the investigation of behavioral biometrics mobile authentication systems, it is worthwhile to focus on how systems can be advanced past previous boundaries and ensure models can hold up against real world contexts. One way to do this is to ensure datasets have larger sample sizes that can properly represent a population and effectively train a ML/DL model.…”

Section: Background and Related Workmentioning

confidence: 99%

Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Kokal,

Vanamala,

Dave

2024

IJACSA

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In recent decades, mobile devices have evolved in potential and prevalence significantly while advancements in security have stagnated. As smartphones now hold unprecedented amounts of sensitive data, there is an increasing need to resolve this gap in security. To address this issue, researchers have experimented with biometric-based authentication methods to improve smartphone security. Following a comprehensive review, it was found that gait-based mobile authentication is under-researched compared to other behavioral biometrics. This study aims to contribute to the knowledge of biometric and gait-based authentication through the analysis of recent gait datasets and their potential with machine learning algorithms. Two recently published gait datasets were used with algorithms such as Random Forest, Decision Tree, and XGBoost to successfully differentiate users based on their respective walking features. Throughout this paper, the datasets, methodology, algorithms, experimental results, and goals for future work will be described.

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Section: Background and Related Workmentioning

confidence: 99%

Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Kokal,

Vanamala,

Dave

2024

IJACSA

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“…Embora o estudo não tenha se voltado especificamente para dispositivos móveis, ele explora em maior detalhe as características identificadas que podem ser úteis ao considerar a avaliac ¸ão em sensores móveis. [Kokal et al 2022] exploram a BBCA por meio de sensores dinâmicos de toque e movimento. Seu estudo se concentrou na determinac ¸ão dos melhores modelos de autenticac ¸ão contínua usando técnicas de classificac ¸ão em ML.…”

Section: Trabalhos Relacionadosunclassified

“…Diante disso, estudos estão sendo desenvolvidos com o objetivo de acrescentar novas camadas de seguranc ¸a aos dispositivos. Uma das áreas de estudo propícia para a inovac ¸ão é a autenticac ¸ão contínua baseada no comportamento do usuário, conhecida como Autenticac ¸ão Contínua Biométrica Comportamental (Behavioral Biometrics for Continuous Authentication -BBCA) [Kokal et al 2022].…”

Section: Introduc ¸ãOunclassified

Behavioral Biometrics for Continuous Authentication on Mobile Devices: Anomaly Detection through Keystroke Dynamics with Machine Learning

Lopes,

Pavanello

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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The popularity of mobile devices generates the need for security solutions to ensure user identity. One approach to deal with this scenario is behavioral biometric for continuous authentication (BBCA), which has become increasingly known with advances in hardware and data science technologies. However, this approach still lacks greater robustness in how to model the user’s behavioral biometrics as well as maximize the effectiveness of this authentication. It is particularly important to note in this context that this paper presents an approach to BBCA using machine learning (ML) techniques integrated with sliding windows. Accordingly, biometric data collected from keystroke dynamics typing activities on mobile devices were used to identify genuine users, random (unskilled) imposters and skilled imposters. The ML models for impostor identification followed an anomaly detection (AD) approach, where only genuine data is available at training time. In addition, the use of sliding windows allowed the inclusion of the temporal dimension of the task. The results obtained indicate that the proposed solution has a practical feasibility in terms of its suitability to perform user identification, specifically, the KNN model stood out by achieving a superior performance in the value window 4. In this configuration, it achieved a score of 94% for the F1-score metric in the random imposter scenario, and 93% in the skilled imposter scenario.

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“…In [1], the authors convey an in-depth survey on which deep learning and machine learning models are used for biometric verification. There is extensive research on hybrid models, such as extracting features with the CNN model and conducting authentication with some machine learning models, such as SVM or One-Class SVM or LSTM block with further Stochastic Gradient Descent (SGD) classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on hybrid transformer-based autoencoders for user biometric verification

Havrylovych,

Danylov

2023

SRIT

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Our current study extends previous work on motion-based biometric verification using sensory data by exploring new architectures and more complex input from various sensors. Biometric verification offers advantages like uniqueness and protection against fraud. The state-of-the-art transformer architecture in AI is known for its attention block and applications in various fields, including NLP and CV. We investigated its potential value for applications involving sensory data. The research proposes a hybrid architecture, integrating transformer attention blocks with different autoencoders, to evaluate its efficacy for biometric verification and user authentication. Various configurations were compared, including LSTM autoencoder, transformer autoencoder, LSTM VAE, and transformer VAE. Results showed that combining transformer blocks with an undercomplete deterministic autoencoder yields the best performance, but model performance is significantly influenced by data preprocessing and configuration parameters. The application of transformers for biometric verification and sensory data appears promising, performing on par with or surpassing LSTM-based models but with lower inference and training time.

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Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication

Cited by 8 publications

References 4 publications

Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning

Behavioral Biometrics for Continuous Authentication on Mobile Devices: Anomaly Detection through Keystroke Dynamics with Machine Learning

Research on hybrid transformer-based autoencoders for user biometric verification

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