User Authentication Method Based on Keystroke Dynamics and Mouse Dynamics with Scene-Irrelated Features in Hybrid Scenes

Wang, Xiujuan; Shi, Yutong; Zheng, Kan; Zhang, Yuyang; Hong, Weijie; Cao, Siwei

doi:10.3390/s22176627

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…Shi, X. Wang, Zheng, and Cao [43] proposed a user authentication method based on KD and mouse dynamics involving comparison of all of the representative time windows and dimensionality-reduction targets of the KD features to determine the parameters for ensuring the robustness of the algorithm and, using real-world setting, the HCI dataset achieved 89.22% accuracy in authenticating users, thus demonstrating the effectiveness of the algorithm. X. Wang, Shi, Zheng, Zhang, Hong, and Cao [44] presented a user authentication method that relies on scene-related and user-related features for user identification: first, features are extracted based on keystroke and mouse movement data; next, scene-related features are obtained that have a low correlation with scenes; lastly, scene-related and user-related features are fused to ensure their integrity. This proposed method has the advantage of improving user authentication accuracy in hybrid scenes, with an accuracy of 84%.…”

Section: Behavioral Authentication Using MLmentioning

confidence: 99%

A Secure and Scalable Behavioral Dynamics Authentication Model

Oladipo,

Awotunde,

Nicho

et al. 2023

IJACSA

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Various authentication methods have been proposed to mitigate data breaches. However, the increasing frequency of data breaches and users' lack of awareness have exposed traditional methods, including single-factor passwordbased systems and, two-factor authentication systems, to vulnerabilities against attacks. While behavioral authentication holds promise in tackling these issues, it faces challenges concerning interoperability between operating systems, the security of behavioral data, accuracy enhancement, scalability, and cost. This research presents a scalable dynamic behavioral authentication model utilizing keystroke typing patterns. The model is constructed around five key components: humancomputer interface devices, encryption of behavioral data, consideration of the authenticator's emotional state, incorporation of cross-platform features, and proposed implementation solutions. It addresses potential typing errors and employs data encryption for behavioral data, achieving a harmonious blend of usability and security by leveraging keyboard dynamics. This is accomplished through the implementation of a web-based authentication system that integrates Convolutional Neural Networks (CNNs) for advanced feature engineering. Keystroke typing patterns were gathered from participants and subsequently employed to evaluate the system's keystroke timing verification, login ID verification, and error handling capabilities. The web-based system uniquely identifies users by merging their username-password (UN-PW) credentials with their keyboard typing patterns, all while securely storing the keystroke data. Given the achievement of a 100% accuracy rate, the proposed Behavioral Dynamics Authentication Model (BDA) introduces future researchers to five scalable constructs. These constructs offer an optimal combination, tailored to the device and context, for maximizing effectiveness. This achievement underscores its potential applications in the realm of authentication.

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Section: Behavioral Authentication Using MLmentioning

confidence: 99%

A Secure and Scalable Behavioral Dynamics Authentication Model

Oladipo,

Awotunde,

Nicho

et al. 2023

IJACSA

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“…Finally, in the field of security, a recent paper [33] used metaheuristics to augment a neural network to improve keystroke dynamics. Touch gestures were used by Stylios et al [34] to improve authentication, while Wang et al [35] used mouse clicks. A key concern of security is preserving privacy, and while keystroke dynamics help with identification and authentication, the nature of the data collected raises privacy concerns [36], which can be ameliorated via privacy preserving methods, such as used by Acar et al in [37]: fuzzy hashing and fully homomorphic encryption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Campisis [12] security smartphone verification Kambourakis [13] security smartphone authenticating Oyebola [15] dataset collection keyboard, smartphone, tablet any Wahab [7] security keyboards verification Alsuhibany [16] security tablet authentication Killourhy [17] clock testing keyboard any Shekhawat [18] security keyboard with sensor authentication Ning [19] health smartphone brain health Senerath [20] security smartphone authentication Tsimperidis [21] HCI keyboard gender, age, handedness Tsimperidis [22] HCI keyboard language Saugbacs [23] emotion smartphone stress Cascone [25] HCI smartphone age, gender, user experience Lamiche [26] security smartphone authentication Roy [28] health keyboard Parkinson's disease Acien [29] health keyboard mental fatigue Roy [30] HCI smartphone age, gender Chen [31] security keyboard authentication Tsimperidis [32] HCI keyboard education level ElKenawy [33] security smartphone authenticating Stylios [34] security smartphone authenticating Wang [35] security keyboard, mouse authenticating Hatin [36] security smartphone authentication with privacy Acar [37] security keyboard authentication with privacy…”

Section: Paper Field Input Type Applicationmentioning

confidence: 99%

Authentication by Keystroke Dynamics: The Influence of Typing Language

Altwaijry

2023

Applied Sciences

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Keystroke dynamics is a biometric method that uses a subject’s typing patterns for authentication or identification. In this paper we investigate typing language as a factor influencing an individual’s keystroke dynamics. Specifically, we discern whether keystroke dynamics is contingent on the spatial arrangement of letters on the keyboard, or alternatively, whether it is influenced by the linguistic characteristics inherent to the language being used. For this purpose, we construct a new dataset called the Bilingual Keystroke Dynamics Dataset in two languages: English and Arabic. The results show that the authentication system is not contingent on the spatial arrangement of the letters, and is primarily influenced by the language being used, and a system that is used by bilingual users must take into account that each user should have two profiles created, one for each language. An average equal error rate of 0.486% was achieved when enrolling in English and testing on Arabic, and 0.475% when enrolling in Arabic and testing on English.

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“…By analyzing patterns in user movements, gait, and other behavioral cues, the model learns to distinguish between genuine and unauthorized individuals. This approach offers several potential advantages over traditional authentication techniques [6][7][8][9]: Continuous Authentication (CA) Unlike one-time authentication methods, which only verify users at specific intervals, the ML model can continuously assess the authenticity of users based on their ongoing activity patterns. This dynamic authentication process adds an extra security layer, reducing the likelihood of unauthorized access.…”

mentioning

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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User Authentication Method Based on Keystroke Dynamics and Mouse Dynamics with Scene-Irrelated Features in Hybrid Scenes

Cited by 5 publications

References 41 publications

A Secure and Scalable Behavioral Dynamics Authentication Model

A Secure and Scalable Behavioral Dynamics Authentication Model

Authentication by Keystroke Dynamics: The Influence of Typing Language

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

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