Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Tewari, Anurag

doi:10.36227/techrxiv.19532269.v1

Cited by 5 publications

(4 citation statements)

References 52 publications

(14 reference statements)

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“…The two main ideas used to make a convolutional neural network particularly successful are sparse connections and weight sharing. According to the study, activation functions (ReLu, Maxout), loss functions (SoftMax, hinge), regularization technique (dropout), optimization method (data augmentation, batch normalization), and fast processing (sparse convolution) were used in conjunction with CNN [53]. As an alternative there are long short-term memory (LSTM) networks and a variation of the LSTM, called a gated recurrent unit (GRU).…”

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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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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“…61 − 98.25] 97.11 Finger -Print [15] [75. 35 − 98.60] 90.6 Finger -Vein [16] [79.00 − 100] 96.3 Handwriting [69] [76.00 − 97.00] 87.23 Hand -Geometry [18] [96.23 − 99.81] 98.7 Keystroke [70] [90. 50 − 99.31] 95.1 Lips Motion [20] [53.00 − 100] 90.65 Palm -Print [71] [97.…”

Section: Biometricsmentioning

confidence: 99%

Robustness Assessment of Biometric Authenticators

Dagnas,

Bkakria,

Yaich

2023

2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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Biometric authenticators aim to provide a safe, secure, and accurate authentication process in restricted areas. Despite their advantages, biometric authenticators are vulnerable to cyber-attacks, such as spoofing attacks. Spoofing attacks enable malicious actors to masquerade as someone else to gain illegitimate access or privilege. To proceed, the attacker forges fake biometric data or duplicates existing ones. In such a context, the evaluation of the robustness of biometric authenticators is paramount to assessing their resilience potential and derive deployment strategies. Through this work, we propose a generic assessment method, based on a metric which quantifies the robustness of biometrics against cyber-attacks. Our methodology can be adapted to different families of cyber-attacks targeting biometric authentication techniques. We demonstrate our approach by considering spoofing-attacks. To achieve this objective, we present an extended state-of-the-art of biometrics (physiological and behavioural), including emerging biometric technologies. We also provide an overview of spoofing-attacks for each identified biometric mechanism in the literature. Based on this knowledge, we quantify and we combine the characteristics of such attacks into a quantitative robustness metric which can be applied to both a single and a combination of authenticators.

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“…They applied the SVM model to the dataset, and their experiment resulted in an accuracy rate of 84% and a false positive rate of 8.77%. Authors in [12] proposed a novel keystroke dynamics-based authentication system that used deep learning techniques, specifically triplet loss, to improve security. The system was tested on a dataset consisting of keystroke samples collected from 90 users and achieved an accuracy rate of 99.06%.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets

Aljahdali

Thabit

Aldissi

et al. 2023

Eng. Technol. Appl. Sci. Res.

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The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach.

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Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Cited by 5 publications

References 52 publications

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

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

Robustness Assessment of Biometric Authenticators

Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets

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