Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics

de‐Marcos, Luis; Martínez-Herráiz, José-Javier; Junquera-Sánchez, Javier; Cilleruelo, Carlos; Pages-Arévalo, Carmen

doi:10.3390/electronics10141622

Cited by 11 publications

(4 citation statements)

References 36 publications

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“…By plotting recall on the y-axis and precision on the x-axis, the recall-precision curve is usually utilized for different threshold values used in classification choices [23][24]. Every point on the curve was associated with a certain threshold.…”

Section: Continuous Authenticationmentioning

confidence: 99%

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Balachandra,

K V

et al. 2024

JMC

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In the ever-evolving realm of technology, the identification of human activities using intelligent devices such as smartwatches, fitness bands, and smartphones has emerged as a crucial area of study. These devices, equipped with inertial sensors, gather a wealth of data and provide insights into users' movements and behaviors. These data not only serve practical purposes, but also hold significant implications for domains such as healthcare and fitness tracking. Traditionally, these devices have been employed to monitor various health metrics such as step counts, calorie expenditure, and real-time blood pressure monitoring. However, recent research has shifted its focus to leveraging the data collected by these sensors for user authentication purposes. This innovative approach involves the utilization of Machine Learning (ML) models to analyze the routine data captured by sensors in smart devices employing ML algorithms, which can recognize and authenticate users based on their unique movement patterns and behaviors. This introduces a paradigm shift from traditional one-time authentication methods to continuous authentication, adding an extra layer of security to protect users against potential threats. Continuous authentication offers several advantages over its conventional counterparts. First, it enhances security by constantly verifying a user's identity through their interaction with the device, thereby mitigating the risk of unauthorized access. Second, it provides a seamless and nonintrusive user experience, eliminating the need for repetitive authentication prompts. Moreover, it offers robust protection against various threats such as identity theft, unauthorized access, and device tampering. The application of continuous authentication extends beyond individual devices and encompasses interconnected systems and networks. This holistic approach ensures a comprehensive security across digital platforms and services. The experiments demonstrate that the logistic regression model achieves an accuracy of 82.32% on the test dataset, highlighting its robustness for binary classification tasks. Additionally, the random forest model outperforms with a 92.18% accuracy, emphasizing its superior capability in handling complex feature interactions. In the study, the sequential neural network achieved an accuracy of 92% on the HAR dataset, outperforming traditional machine learning models by a significant margin. The model also demonstrated robust generalization capabilities with a minimal drop in performance across various cross-validation folds.

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Section: Continuous Authenticationmentioning

confidence: 99%

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Balachandra,

K V

et al. 2024

JMC

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show abstract

“…As both surveys were published in 2013, newer approaches are not included. Newer publications either propose an approach (e. g., (Kim et al, 2020)) or concentrate on specific issues, including a comparison of different models (Singh et al, 2020), machine learning classifiers (de Marcos et al, 2021), and emotion recognition (Maalej and Kallel, 2020). In addition, Shekhawat and Bhatt (Shekhawat and Bhatt, 2019) analyze the ERR in multiple use cases and different classification algorithms without converting the validation parameters for comparison.…”

Section: Related Workmentioning

confidence: 99%

Evaluation Scheme to Analyze Keystroke Dynamics Methods

Dimaratos

Pöhn

2023

Proceedings of the 9th International Conference on Information Systems Security and Privacy

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Password authentication is a weak point for security as passwords are easily stolen and a user may ignore the security by using a simple password. Therefore, services increasingly demand a second factor. While this may enhance security, it comes with a lower level of usability and another factor to be forgotten. A smartphone is an important device in daily life. With the growing number of sensors and features in a smartphone, keystroke dynamics may provide an easy-to-use method. In this paper, we introduce requirements for biometric authentication and keystroke dynamics. This results in an evaluation scheme, which is applied to three selected approaches. Based on the comparison, keystroke dynamics and the evaluation scheme are discussed. The obtained results indicate that keystroke dynamics can be used as another authentication method but can be bypassed by stronger adversaries. For further research, a common data set would improve the comparability.

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“…Some research provides lightweight mechanisms to support encryption and authentication for real-world scenarios. In order to better realize the integration and development of continuous authentication (CA) [28] in mobile devices, M. Frank et al [29] implement and compare different ML agent models for CA in mobile environments. Experiments show that among the mentioned ML classifiers, ensemble methods (RFC, ETC, and GBC) perform the best, while SVM performs the worst among all classifiers.…”

Section: Encrypted Communication Technology and Mechanismmentioning

confidence: 99%

MultiSec: A Multi-Protocol Security Forwarding Mechanism Based on Programmable Data Plane

et al. 2022

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With the development of network technology, various network protocols different from TCP/IP have emerged. The heterogeneous integrated network has been proposed to realize the interconnection between heterogeneous networks running different protocols. However, current protocol conversion mechanisms often can only handle a few pre-defined protocols and do not support the flexible expansion of new protocols, which cannot meet the needs of the efficient convergence of different heterogeneous networks. Addirionally, due to the lack of security mechanisms, data in the core network is confronted with the risk of stealing and tampering. Our aim is to provide a protocol-extensible protocol conversion and secure transmission integration mechanism, MultiSec, for heterogeneous converged networks. First, based on the programmable data plane, the parser is reconfigured to realize multi-protocol parsing. Furthermore, the encryption mechanism implemented in the P4 extern is proposed and unified to the data plane together with the protocol conversion mechanism. Finally, the MultiSec prototype is implemented on a programmable software switch and accelerated by a dedicated encryption card. Experiments show that MultiSec successfully realizes multi-protocol conversion and data encryption, and the system performance is significantly improved with the help of an encryption card.

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Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics

Cited by 11 publications

References 36 publications

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Evaluation Scheme to Analyze Keystroke Dynamics Methods

MultiSec: A Multi-Protocol Security Forwarding Mechanism Based on Programmable Data Plane

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