Dynamic keystroke pattern analysis and classifiers with competence for user recognition

Porwik, Piotr; Doroz, Rafał; Wesołowski, T

doi:10.1016/j.asoc.2020.106902

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…We cannot perform a comparison with other SOTA methods in terms of performance for more than one hundred users since this information, although interesting, is not available. Finally, since Table 1 shows that the only approach that provides better results than our solution, in terms of EER, is the one proposed in Porwik, Doroz & Wesolowski (2021), we can say that, differently from this work, this performs intrusion detection using binary classification and is not able to identify the typing user within a large dataset.…”

Section: Classification Resultsmentioning

confidence: 83%

Continuous authentication using deep neural networks ensemble on keystroke dynamics

Bernardi

Cimitile

Pecori

2021

PeerJ Computer Science

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During the last years, several studies have been proposed about user identification by means of keystroke analysis. Keystroke dynamics has a lower cost when compared to other biometric-based methods since such a system does not require any additional specific sensor, apart from a traditional keyboard, and it allows the continuous identification of the users in the background as well. The research proposed in this paper concerns (i) the creation of a large integrated dataset of users typing on a traditional keyboard obtained through the integration of three real-world datasets coming from existing studies and (ii) the definition of an ensemble learning approach, made up of basic deep neural network classifiers, with the objective of distinguishing the different users of the considered dataset by exploiting a proper group of features able to capture their typing style. After an optimization phase, in order to find the best possible base classifier, we evaluated the ensemble super-classifier comparing different voting techniques, namely majority and Bayesian, as well as training allocation strategies, i.e., random and K-means. The approach we propose has been assessed using the created very large integrated dataset and the obtained results are very promising, achieving an accuracy of up to 0.997 under certain evaluation conditions.

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Section: Classification Resultsmentioning

confidence: 83%

Continuous authentication using deep neural networks ensemble on keystroke dynamics

Bernardi

Cimitile

Pecori

2021

PeerJ Computer Science

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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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“…One recent study comparing classifiers demonstrated that an EER of 10% is achievable using a single classifier and that it is even possible to achieve an EER below 1% when using an ensemble of classifiers [31]. In this work, the aim is to gain an understanding of the relationship between the characteristics of the password, biometric capabilities, and the number of repetitions that the user has undertaken.…”

Section: Biometric Systemmentioning

confidence: 99%

An empirical analysis of keystroke dynamics in passwords: A longitudinal study

et al. 2022

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The use of keystroke timings as a behavioural biometric in fixed-text authentication mechanisms has been extensively studied. Previous research has investigated in isolation the effect of password length, character substitution, and participant repetition. These studies have used publicly available datasets, containing a small number of passwords with timings acquired from different experiments. Multiple experiments have also used the participant's first and last name as the password; however, this is not realistic of a password system. Not only is the user's name considered a weak password, but their familiarity with typing the phrase minimises variation in acquired samples as they become more familiar with the new password. Furthermore, no study has considered the combined impact of length, substitution, and repetition using the same participant pool. This is explored in this work, where the authors collected timings for 65 participants, when typing 40 passwords with varying characteristics, 4 times per week for 8 weeks. A total of 81,920 timing samples were processed using an instance-based distance and threshold matching approach. Results of this study provide empirical insight into how a password policy should be created to maximise the accuracy of the biometric system when considering substitution type and longitudinal effects.

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Dynamic keystroke pattern analysis and classifiers with competence for user recognition

Cited by 11 publications

References 20 publications

Continuous authentication using deep neural networks ensemble on keystroke dynamics

Continuous authentication using deep neural networks ensemble on keystroke dynamics

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

An empirical analysis of keystroke dynamics in passwords: A longitudinal study

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