Supporting unknown number of users in keystroke dynamics models

Hazan, Itay; Margalit, Oded; Rokach, Lior

doi:10.1016/j.knosys.2021.106982

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…In [ 15 ], the limitations of keystroke dynamics algorithms when multiple users share an account were addressed. The authors introduced a four-stage approach that utilizes pre-existing algorithms to autonomously ascertain the number of users who share an account and provide reliable support for accounts that are shared by multiple users.…”

Section: Related Workmentioning

confidence: 99%

“…These findings further underscore the superior performance and efficacy of our proposed approach in comparison to earlier methods. Comparison of EER with the related literature (from left to right: [15], [18], [16], [24], [27], [14], [26], [13], [11]).…”

Section: Robustnessmentioning

confidence: 99%

“…Figure 20. Comparison of EER with the related literature (from left to right:[15],[18],[16],[24],[27],[14],[26],[13],[11]). …”

mentioning

confidence: 99%

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Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication

AbdelRaouf

Chelloug

Muthanna

et al. 2023

Sensors

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The safeguarding of online services and prevention of unauthorized access by hackers rely heavily on user authentication, which is considered a crucial aspect of security. Currently, multi-factor authentication is used by enterprises to enhance security by integrating multiple verification methods rather than relying on a single method of authentication, which is considered less secure. Keystroke dynamics is a behavioral characteristic used to evaluate an individual’s typing patterns to verify their legitimacy. This technique is preferred because the acquisition of such data is a simple process that does not require any additional user effort or equipment during the authentication process. This study proposes an optimized convolutional neural network that is designed to extract improved features by utilizing data synthesization and quantile transformation to maximize results. Additionally, an ensemble learning technique is used as the main algorithm for the training and testing phases. A publicly available benchmark dataset from Carnegie Mellon University (CMU) was utilized to evaluate the proposed method, achieving an average accuracy of 99.95%, an average equal error rate (EER) of 0.65%, and an average area under the curve (AUC) of 99.99%, surpassing recent advancements made on the CMU dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Robustnessmentioning

confidence: 99%

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Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication

AbdelRaouf

Chelloug

Muthanna

et al. 2023

Sensors

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“…Keystroke dynamics does not require any additional hardware as required by other biometric authentication methods, such as fingerprints or facial recognition. Personal keystroke biometrics is difficult to forge and could be used for authentication [34,35]. For example, the online learning platform Coursera applies this method to authenticate online students [36].…”

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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“…The clustering techniques are utilized in user authenticatoin which have proved efficient results. The X-means is used in [19] which is configured with two parameters. The two parameters are configured with 100 and 10 which refer to the max number of iterations and the max number of clusters respectively.…”

Section: Related Workmentioning

confidence: 99%

Keystroke dynamics based user authentication using Histogram Gradient Boosting

Ibrahim

AbdelRaouf²,

Amin³

et al. 2022

IJCI. International Journal of Computers and Information

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User authentication is a vital part of securing digital services and preventing unauthorized users from gaining access to the system. Nowadays, organizations use Multi-Factor Authentication (MFA) to provide robust protection by utilizing two or more identity procedures instead of using Single Factor Authentication (SFA) which became less secure. Keystroke dynamics is a behavioral biometric that examines a user's typing rhythm to determine the subject's legitimacy using the system. Keystroke dynamics have a minimal implementation cost and do not need special hardware in the authentication process since the gathering of typing data is reasonably straightforward and does not involve additional effort from the user. In this work, we present an efficient approach that uses the quantile transformation that transforms data distribution into uniform distribution which significantly reduces the impact of outlier and extreme values. Histogram Gradient Boosting is employed as the primary classifier for the training and testing phase. Our proposed approach is evaluated on Carnegie Mellon University (CMU) keystroke benchmark dataset which has achieved 97.96% of average accuracy and 0.014 of average equal error rate (EER) across all subjects which outperforms all the previous advances in both machine and deep learning approaches.

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Supporting unknown number of users in keystroke dynamics models

Cited by 8 publications

References 17 publications

Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication

Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication

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

Keystroke dynamics based user authentication using Histogram Gradient Boosting

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