Keystroke Dynamics: Concepts, Techniques, and Applications

Shadman, Rashik; Wahab, Ahmed Anu; Manno, Michael; Lukaszewski, Matthew; Hou, Daqing; Hussain, Faraz

doi:10.48550/arxiv.2303.04605

Cited by 2 publications

(2 citation statements)

References 105 publications

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“…Free-text is more dynamic and uses a self-adaptive dataset. Typically, users are asked to type a few paragraphs of text or the required data could be collected simply by monitoring how the user interacts with a keyboard on a daily basis [45]. These systems are used for continuous authentication and are often used to defend against keystroke-injection attacks.…”

Section: Keystroke Dynamics and Its Circumventionmentioning

confidence: 99%

A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

Gurčinas,

Dautartas,

Janulevičius

et al. 2023

Electronics

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Investigation and detection of cybercrimes has been in the spotlight of cybersecurity research for as long as the topic has existed. Modern methods are required to keep up with the pace of the technology and toolset used to facilitate these crimes. Keystroke-injection attacks have been an issue due to the limitations of hardware and software up until recently. This paper presents comprehensive research on keystroke-injection payload generation that proposes the use of deep learning to bypass the security of keystroke-based authentication systems focusing on both fixed-text and free-text scenarios. In addition, it specifies the potential risks associated with keystroke-injection attacks. To ensure the legitimacy of the investigation, a model is proposed and implemented within this context. The results of the implemented implant model inside the keyboard indicate that deep learning can significantly improve the accuracy of keystroke dynamics recognition as well as help to generate effective payload from a locally collected dataset. The results demonstrate favorable accuracy rates, with reported performance of 93–96% for fixed-text scenarios and 75–92% for free-text. Accuracy across different text scenarios was achieved using a small dataset collected with the proposed implant model. This dataset enabled the generation of synthetic keystrokes directly within a low-computation-power device. This approach offers efficient and almost real-time keystroke replication. The results obtained show that the proposed model is sufficient not only to bypass the fixed-text keystroke dynamics system, but also to remotely control the victim’s device at the appropriate time. However, such a method poses high security risks when deploying adaptive keystroke injection with impersonated payload in real-world scenarios.

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Section: Keystroke Dynamics and Its Circumventionmentioning

confidence: 99%

A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

Gurčinas,

Dautartas,

Janulevičius

et al. 2023

Electronics

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show abstract

“…In a concluding paper, Shadman et al [21] provided an overview of the concepts, techniques, and applications of keystroke dynamics. It covers the background and history of keystroke dynamics analysis, different approaches to collecting and processing keystroke data, different types of keystroke dynamics analysis, and their applications in different domains such as security, human-computer interaction, and health monitoring.…”

Section: Introductionmentioning

confidence: 99%

The Way We Type Reveals Our Native Language

Tsimperidis,

Grunova,

Papakostas

2023

IEEE Access

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Knowing some characteristics of an unknown user is useful information for security and commercial purposes. One of the acquired characteristics is the user's native language, and its recognition can be achieved with data derived from the text he/she types, since text is the most widespread means of communication between Internet users. Keystroke dynamics, which leverages data derived from how user types, ensures that no sensitive data are leaked. In this work, data from the daily typing of users of five different native languages are collected, keystroke dynamics features are extracted, the most suitable ones are selected using a feature selection algorithm, well-known machine learning models and a boosting algorithm are used, and a rate of correct prediction that exceeds 90% is achieved. Knowing a user's native language can help strengthen authentication systems, make better use of online services, and protect unsuspecting users from falling victim to fraud.

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Keystroke Dynamics: Concepts, Techniques, and Applications

Cited by 2 publications

References 105 publications

A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

The Way We Type Reveals Our Native Language

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