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Article

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

Department of Information Systems, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
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Author to whom correspondence should be addressed.
Electronics 2023, 12(13), 2894; https://doi.org/10.3390/electronics12132894
Submission received: 7 June 2023 / Revised: 26 June 2023 / Accepted: 28 June 2023 / Published: 30 June 2023
(This article belongs to the Special Issue Data Driven Security)

Abstract

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.
Keywords: keystroke dynamics; machine learning; deep learning; behavioral biometrics; keystroke recognition keystroke dynamics; machine learning; deep learning; behavioral biometrics; keystroke recognition

Share and Cite

MDPI and ACS Style

Gurčinas, V.; Dautartas, J.; Janulevičius, J.; Goranin, N.; Čenys, A. A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation. Electronics 2023, 12, 2894. https://doi.org/10.3390/electronics12132894

AMA Style

Gurčinas V, Dautartas J, Janulevičius J, Goranin N, Čenys A. A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation. Electronics. 2023; 12(13):2894. https://doi.org/10.3390/electronics12132894

Chicago/Turabian Style

Gurčinas, Vitalijus, Juozas Dautartas, Justinas Janulevičius, Nikolaj Goranin, and Antanas Čenys. 2023. "A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation" Electronics 12, no. 13: 2894. https://doi.org/10.3390/electronics12132894

APA Style

Gurčinas, V., Dautartas, J., Janulevičius, J., Goranin, N., & Čenys, A. (2023). A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation. Electronics, 12(13), 2894. https://doi.org/10.3390/electronics12132894

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