On mouse dynamics as a behavioral biometric for authentication

Jorgensen, Zach; Yu, Ting

doi:10.1145/1966913.1966983

Cited by 129 publications

(75 citation statements)

References 12 publications

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“…They could thus be used in conjunction, in an attempt to develop a more precise approach. Other approaches have been analyzed by [18], who review existing authentication approaches based on mouse dynamics and shed light on some important limitations regarding how the effectiveness of these approaches has been evaluated in the past. The authors also present the results of several experiments conducted by them to illustrate their observations and suggest guidelines for evaluating future authentication approaches based on mouse dynamics.…”

Section: Related Workmentioning

confidence: 99%

Quantifying the effects of external factors on individual performance

Carneiro

Nováis

2017

Future Generation Computer Systems

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Monitoring and managing performance in the workplace is nowadays an important aspect, in a time in which methodologies like Agile push individual and team limits further. Current performance monitoring approaches are either intrusive or based on productivity measures and are thus often dreaded by workers. Moreover, these approaches do not take into account the importance and role of the numerous external factors that influence productivity. We present a non-intrusive performance monitoring environment based on behavioral biometrics and real time analytics. It monitors and analyses 15 features extracted from the workers' interaction with the computer and can provide a measure of performance that is completely transparent. This measure is sensitive to external factors such as mental fatigue, stress or emotional valence. We validate this environment by assessing the effects of musical selection on Human-Computer Interaction. Results show a significant improvement on mouse motion when participants listen to the selected auditory stimuli and a negative effect on typing performance, especially with stimuli with positive tension. This work will enable the development of performance monitoring and management environments, with benefits for both organizations and individuals.

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Section: Related Workmentioning

confidence: 99%

Quantifying the effects of external factors on individual performance

Carneiro

Nováis

2017

Future Generation Computer Systems

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“…Asha & Chellappan [4] recommend merging behavioral and physiological biometrics by using a physical fingerprint recognition combined with mouse dynamics (both biometrics authentication achieved using a mouse with an inbuilt fingerprint scanner). Many practical studies argue that the time required for data collection of mouse dynamics is very long [7]; this would open the door for suspicious student activities. Additionally, the system ignores the other potential problems including the secure environment around the student (e.g.…”

Section: Related Workmentioning

confidence: 99%

A Robust e-Invigilation System Employing Multimodal Biometric Authentication

Ketab¹,

Clarke²,

Dowland³

2017

IJIET

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Abstract-The significant growth in users of e-learning technologies and their use in courses has given rise to a major concern over protecting them from misuse; a significant concern is that of the potential for cheating or illicit assistance during online examinations. This paper presents the development of robust, flexible, transparent and continuous authentication mechanism for e-assessments. To monitor the exam taker and ensure that only the legitimate student is taking the exam, the system offers a continuous user identification employing multimodal biometrics; a security layer using an eye tracker to record the student's eye movement; and, speech recognition to detect inappropriate communication. The focus of this paper in particular is the development and evaluation of 3D facial authentication. An experiment has been conducted to investigate the ability of the proposed platform to detect any cheating attempts. During the experiment, participants' biometric data, eye movement, and head movements have been collected using custom software. The 3D camera also captured the session using a built-in microphone and the system recognized speech (employing a speech recognition algorithm). 51 participants participated in this experiment. The FRR of all legitimate participants was 0 and 0.0063 in 2D and 3D facial recognition modes respectively. Furthermore, three participants were tasked with a series of eight scenarios that map to typical misuse. The results of the FAR and FRR of five of these threat scenarios in both 2D and 3D mode were 0 with two cases exhibiting an FAR of 0.11 and 0.076 in the 2D mode.

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“…Various challenges have been addressed for verifying mouse actions. As pointed out by (Jorgensen & Yu, 2011), the amount of mouse data and time required for user authentication could affect the practicality of mouse dynamics especially for continuous verification. (Shen, Cai, Guan, Du, & Maxion, 2013) There are two categories for handwritten signature authentication, offline and online.…”

Section: ) Hand Gesturesmentioning

confidence: 99%

Authentication systems: principles and threats

Abdulkader¹,

Atia²,

Mostafa³

2015

CIS

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Identity manipulation is considered a serious security issue that has been enlarged with the spread of automated systems that could be accessed either locally or remotely. Availability, integrity, and confidentiality represent the basic requirements that should be granted for successful authentication systems. Personality verification has taken multiple forms depending on different possession types. They are divided into knowledge based, token based, and biometric based authentication. The permanent ownership to the human being has increased the chances of deploying biometrics based authentication in highly secure systems. It includes capturing the biological traits, which are physiological or behavioral, extracting the important features and comparing them to the previously stored features that belong to the claimed user. Various kinds of attacks aim to take down the basic requirements at multiple points. This paper describes different types of authentication along with their vulnerable points and threatening attacks. Then it provides more details about the biometric system structure as well as examples of distinguishing biological characteristics, organized by their locations. It shows the performance results of various biometric systems along with the deployed algorithms for different components.

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On mouse dynamics as a behavioral biometric for authentication

Cited by 129 publications

References 12 publications

Quantifying the effects of external factors on individual performance

Quantifying the effects of external factors on individual performance

A Robust e-Invigilation System Employing Multimodal Biometric Authentication

Authentication systems: principles and threats

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