Finite Context Modeling of Keystroke Dynamics in Free Text

González, Nahuel; Calot, Enrique P.

doi:10.1109/biosig.2015.7314606

Cited by 8 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, this password was chosen as an example of a strong 10-character password, which meets common requirements for password security. The dataset compiled by Gonzalez comprises a combination of publicly accessible keystroke datasets [56][57][58]. It encompasses both authentic humangenerated keystrokes and synthesized forgeries [59].…”

Section: User Keypress Data and Their Minimummentioning

confidence: 99%

“…The first dataset included in the dataset collection was from CMU [56]. The second dataset was collected from individuals performing daily tasks in an enterprise setup and was used for evaluation of free-text keystroke dynamics for authentication [57]. The third dataset, obtained from anonymous subjects through a crowd-sourcing platform, was aimed at identifying indicators of fraudulent intent by analyzing variations in typing patterns [58].…”

Section: User Keypress Data and Their Minimummentioning

confidence: 99%

See 1 more Smart Citation

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

Gurčinas,

Dautartas,

Janulevičius

et al. 2023

Electronics

View full text Add to dashboard Cite

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.

show abstract

Section: User Keypress Data and Their Minimummentioning

confidence: 99%

Section: User Keypress Data and Their Minimummentioning

confidence: 99%

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

Gurčinas,

Dautartas,

Janulevičius

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…The dataset LSIA is the same as previously used in [37], which is a longer and harsher version than the one used in [38]. It consists of typing sessions recorded during daily work in a healthcare environment for more than a year where the users, mostly doctors, worked rotating shifts and on duty.…”

Section: Datasetsmentioning

confidence: 99%

On the shape of timings distributions in free-text keystroke dynamics profiles

González

Calot

Ierache

et al. 2021

Heliyon

Self Cite

View full text Add to dashboard Cite

Keystroke dynamics is a soft biometric trait. Although the shape of the timing distributions in keystroke dynamics profiles is a central element for the accurate modeling of the behavioral patterns of the user, a simplified approach has been to presuppose normality. Careful consideration of the individual shapes for the timing models could lead to improvements in the error rates of current methods or possibly inspire new ones. The main objective of this study is to compare several heavy-tailed and positively skewed candidate distributions in order to rank them according to their merit for fitting timing histograms in keystroke dynamics profiles. Results are summarized in three ways: counting how many times each candidate distribution provides the best fit and ranking them in order of success, measuring average information content, and ranking candidate distributions according to the frequency of hypothesis rejection with an Anderson-Darling goodness of fit test. Seven distributions with two parameters and seven with three were evaluated against three publicly available free-text keystroke dynamics datasets. The results confirm the established use in the research community of the log-normal distribution, in its two-and three-parameter variations, as excellent choices for modeling the shape of timings histograms in keystroke dynamics profiles. However, the log-logistic distribution emerges as a clear winner among all two-and three-parameter candidates, consistently surpassing the log-normal and all the rest under the three evaluation criteria for both hold and flight times.

show abstract