Time–frequency analysis of keystroke dynamics for user authentication

Toosi, Ramin; Akhaee, Mohammad Ali

doi:10.1016/j.future.2020.09.027

Cited by 14 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results demonstrate the robustness and accuracy of the proposed authentication method. Toosi et al [32] presented a user authentication system based on keyboard dynamics, which operates at a novel level of analysis. The system leverages time-frequency analysis to directly measure the similarity between the input model and the user's reference model.…”

Section: Related Workmentioning

confidence: 99%

Optimizing dynamic keystroke pattern recognition with hybrid deep learning technique and multiple soft biometric factors

Venkatachalam,

Rajiv Kannan

2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

In this work, we propose an optimization approach for dynamic keystroke pattern recognition by leveraging a hybrid deep learning technique and multiple soft biometric factors. Our methodology begins with the introduction of a novel algorithm called dynamic drone squadron optimization (DDSO) to optimize the selection of optimal features from a pool of multiple keystroke features. We then present an enhanced version of the improved sperm swarm optimization (ISSO) algorithm, which effectively combines the optimal weight features derived from multiple biometric responses. Furthermore, we introduce the multi-stage recurrent neural network (MS-RNN) classifier to accurately recognize and classify keystroke patterns. The performance of our proposed ISSO+MS-RNN technique is evaluated using the benchmark KBOC dataset to validate its effectiveness. Comparative analysis is conducted against existing state-of-the-art techniques, employing various evaluation measures, to demonstrate the superior performance of proposed approach.`

show abstract

Section: Related Workmentioning

confidence: 99%

Optimizing dynamic keystroke pattern recognition with hybrid deep learning technique and multiple soft biometric factors

Venkatachalam,

Rajiv Kannan

2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

show abstract

“…And Down-to-down time (DD.time) measures the duration between one keypress to another, Up-to-down time (UD.time) is the time from the release of one key and the press of another (in some literature, called flight time) [34]. The data-collection and training phase of keystroke dynamics can be categorized based on the length and type of the text [54]. Fixed-length models use a limited dataset that consist of username and password.…”

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

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

“…All behavioral characteristics, including the KD, can gradually change with time; however, the probability of stealing or imitating these data is significantly lower than that for physiological data. That is why the KD can be useful for identification and authentication [ 11 , 12 ]. The advantages, disadvantages, and examples of the authentication methods considered above are summarized in Table 1 .…”

Section: Keystroke Authentication and Identificationmentioning

confidence: 99%

“…The analysis of the researches devoted to keystroke features showed that the most popular ones are DU and UD (dwell time and flight time): according to [ 12 , 20 ], the frequency of using each of them ranges from 30% to 40% in applied research.…”

Section: Hidden Monitoring In Online Examination Systemsmentioning

confidence: 99%

Hidden Monitoring Based on Keystroke Dynamics in Online Examination System

Kochegurova

Zateev

2022

Program Comput Soft

View full text Add to dashboard Cite

The COVID-19 pandemic has accelerated the development of distance learning technologies, where online tests and exams play an important role. In online testing, the detection of various types of academic fraud, including cases where the examinee is falsely represented by another person, is of particular importance. Continuous biometric (behavioral) authentication can be a solution to counter unauthorized access. This paper proposes an authentication technology based on keystroke dynamics and hidden monitoring. An application for collecting and updating keystroke dynamics profiles of domain users and their continuous authentication is developed. The efficiency of reducing the dimension of the keystroke feature space based on the frequency of alphabetic letters is demonstrated. Results of popular performance metrics (FAR, FRR, ERR, ROC, and DET) are significantly improved already when evaluating only metric distances. For instance, ERR is reduced from 10.1% to 0.79%, which is comparable to the estimates provided by the kNN method for its optimal parameters.

show abstract

Time–frequency analysis of keystroke dynamics for user authentication

Cited by 14 publications

References 26 publications

Optimizing dynamic keystroke pattern recognition with hybrid deep learning technique and multiple soft biometric factors

Optimizing dynamic keystroke pattern recognition with hybrid deep learning technique and multiple soft biometric factors

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

Hidden Monitoring Based on Keystroke Dynamics in Online Examination System

Contact Info

Product

Resources

About