An Efficient User Verification System Using Angle-Based Mouse Movement Biometrics

Zheng, Nan; Paloski, Aaron; Wang, Haining

doi:10.1145/2893185

Cited by 46 publications

(18 citation statements)

References 23 publications

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“…Everitt and Mcowan [11] found and proved the feasibility of knowing a user's identity by analysing the user's mouse operation habit and behaviour characteristics. With the development of computer GUI, the mouse has superseded the keyboard and become the dominant I/O device [12].…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

Guan

Zhang

2021

Security and Communication Networks

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Most of the current authentication mechanisms adopt the “one-time authentication,” which authenticate users for initial access. Once users have been authenticated, they can access network services without further verifications. In this case, after an illegal user completes authentication through identity forgery or a malicious user completes authentication by hijacking a legitimate user, his or her behaviour will become uncontrollable and may result in unknown risks to the network. These kinds of insider attacks have been increasingly threatening lots of organizations, and have boosted the emergence of zero trust architecture. In this paper, we propose a Multimodal Fusion-based Continuous Authentication (MFCA) scheme, which collects multidimensional behaviour characteristics during the online process, verifies their identities continuously, and locks out the users once abnormal behaviours are detected to protect data privacy and prevent the risk of potential attack. More specifically, MFCA integrates the behaviours of keystroke, mouse movement, and application usage and presents a multimodal fusion mechanism and trust model to effectively figure out user behaviours. To evaluate the performance of the MFCA, we designed and implemented the MFCA system and the experimental results show that the MFCA can detect illegal users in quick time with high accuracy.

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Section: Related Workmentioning

confidence: 99%

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

Guan

Zhang

2021

Security and Communication Networks

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“…in cases when the set of possible users is limited. In simple settings, user recognition is based on a single mouse trajectory [6], [7], [8], [9], [10], [11], [12] or movements performed during a time interval of fixed length [13]. Other studies concluded that not all users can be identified with high precision solely based on a single mouse trajectory [10], [14].…”

Section: A Mouse Trajectory Analysismentioning

confidence: 99%

SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories

Antal¹,

Búza

Fejer

2021

IEEE Access

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The growing interest in bot detection can be attributed to the fact that fraudulent actions performed by bots cause surprisingly high economical damage. State-of-the-art bots aim at mimicking as many as possible aspects of human behavior, ranging from response times and typing dynamics to humanlike phrasing and mouse trajectories. In order to support research on bot detection, in this paper, we propose an approach to generate human-like mouse trajectories, called SapiAgent. To implement SapiAgent, we employ deep autoencoders and a novel training algorithm. We performed experiments on our publicly available SapiMouse dataset which contains human mouse trajectories collected from 120 subjects. The results show that SapiAgent is able to generate more realistic mouse trajectories compared with Bézier curves and conventional autoencoders.

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“…Zheng et al also investigated the user authentication problem in their studies [13], [14]. They proposed some novel features such as angle based metrics.…”

Section: Related Workmentioning

confidence: 99%

Mouse dynamics based user recognition using deep learning

Antal

Fejer

2020

Acta Universitatis Sapientiae, Informatica

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Behavioural biometrics provides an extra layer of security for user authentication mechanisms. Among behavioural biometrics, mouse dynamics provides a non-intrusive layer of security. In this paper we propose a novel convolutional neural network for extracting the features from the time series of users’ mouse movements. The effect of two preprocessing methods on the performance of the proposed architecture were evaluated. Different training types of the model, namely transfer learning and training from scratch, were investigated. Results for both authentication and identification systems are reported. The Balabit public data set was used for performance evaluation, however for transfer learning we used the DFL data set. Comprehensive experimental evaluations suggest that our model performed better than other deep learning models. In addition, transfer learning contributed to the better performance of both identification and authentication systems.

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An Efficient User Verification System Using Angle-Based Mouse Movement Biometrics

Cited by 46 publications

References 23 publications

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories

Mouse dynamics based user recognition using deep learning

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