Text-Based Active Authentication for Mobile Devices

Saevanee, Hataichanok; Clarke, Nathan; Furnell, Steven; Biscione, Valerio

doi:10.1007/978-3-642-55415-5_9

Cited by 22 publications

(8 citation statements)

References 18 publications

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“…They used keystroke latencies and key-hold times, but no touch features. Saevanee et al [47] combined linguistic analysis with keystroke dynamics on mobile devices. The keystroke-based part of their system used key-hold times and achieved 20.8% EER.…”

Section: Behavioural Biometrics For Mobile Typingmentioning

confidence: 99%

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Buschek¹,

Luca²,

Alt³

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

102

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Authentication methods can be improved by considering implicit, individual behavioural cues. In particular, verifying users based on typing behaviour has been widely studied with physical keyboards. On mobile touchscreens, the same concepts have been applied with little adaptations so far. This paper presents the first reported study on mobile keystroke biometrics which compares touch-specific features between three different hand postures and evaluation schemes. Based on 20.160 password entries from a study with 28 participants over two weeks, we show that including spatial touch features reduces implicit authentication equal error rates (EER) by 26.4 -36.8% relative to the previously used temporal features. We also show that authentication works better for some hand postures than others. To improve applicability and usability, we further quantify the influence of common evaluation assumptions: known attacker data, training and testing on data from a single typing session, and fixed hand postures. We show that these practices can lead to overly optimistic evaluations. In consequence, we describe evaluation recommendations, a probabilistic framework to handle unknown hand postures, and ideas for further improvements.

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Section: Behavioural Biometrics For Mobile Typingmentioning

confidence: 99%

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Buschek¹,

Luca²,

Alt³

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

102

View full text Add to dashboard Cite

show abstract

“…The focus was then shifted to deploying various aspects of behavioral profiling as in [129,131]. The former study accomplished an EER of 5.4%, 2.2% and 13.5% when utilized the usage of calling, text messaging, and general applications respectively with an overall of 7.03% EER.…”

Section: Behavioral Multimodal Systemsmentioning

confidence: 99%

Continuous and transparent multimodal authentication: reviewing the state of the art

et al. 2015

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Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. They are also still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. This is followed by a literature review of the existing research on continuous and transparent multimodal authentication. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level. Ultimately, a potential federated biometric authentication solution is presented; however it needs to be developed and extensively evaluated, thus operating in a transparent, continuous and user-friendly manner.

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“…Among further studies in a similar context, Saevanee et al [17] examined the combination of three diverse biometric methods: keystroke dynamics, behavioural profiling and linguistic profiling from 30 virtual users (the dataset was not real and was gathered from different datasets). To continue their work, Saevanee et al [18] presented a text-based authentication framework utilising the above modalities and introduced a security aspect by allowing the user to set security levels for access to different applications. In other recent work, Fridman et al [19] proposed a parallel binary decision-level fusion architecture for active authentication.…”

Section: Related Workmentioning

confidence: 99%

A New User-Apps Interactions Dataset for Behavioral Profiling Using Smartphones

Alotaibi¹,

Alruban²,

Alotaibi³

et al. 2019

JITST

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The use of mobile devices in our daily lives has grown steadily. These mobile devices contain sensitive data such as text messages, photos, communication logs, contact lists, personal information and stored passwords. They are also used to perform activities such as sending emails or transferring money via mobile Internet banking, which is considered a sensitive process. As a consequence, more security is needed on mobile devices since, after point-of-entry authentication, the user can perform almost all tasks without having to re-authenticate. On the other hand, many authentication methods have been suggested to improve the security of mobile devices in a transparent and continuous manner, providing a basis for convenient and secure user reauthentication. In addition, although a number of studies have investigated the feasibility of using behavioural biometrics to secure a mobile device, there is a lack of studying user behavioural profiling interactions with their smartphones due to there are no such datasets available. The main aim of this paper is to present a new user-apps Interactions dataset for behavioral profiling using Smartphones which might help researchers to improve smartphones security. A study involving data collected from 76 users over a 1-month period was conducted, generating over 3 million actions based on users' interactions with their smartphone. This study also demonstrates and highlights some future work by utilizing the acquired dataset to provide robust, continuous and transparent authentication and usable system as well.

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Text-Based Active Authentication for Mobile Devices

Cited by 22 publications

References 18 publications

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Continuous and transparent multimodal authentication: reviewing the state of the art

A New User-Apps Interactions Dataset for Behavioral Profiling Using Smartphones

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