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Journal articles on the topic 'Keystroke dynamic authentication'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Yunanto, Prasti Eko, and Ari Moesriami Barmawi. "Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram." Jurnal Ilmu Komputer dan Informasi 15, no. 2 (2022): 81–91. http://dx.doi.org/10.21609/jiki.v15i2.1015.

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Currently, most of the smartphones recognize uses based on static biometrics, such as face and fingerprint. However, those traits were vulnerable against spoofing attack. For overcoming this problem, dynamic biometrics like the keystroke and gaze are introduced since it is more resistant against spoofing attack. This research focuses on keystroke dynamics for strengthening the user recognition system against spoofing attacks. For recognizing a user, the user keystrokes feature used in the login process is compared with keystroke features stored in the keystroke features database. For evaluating the accuracy of the proposed system, words generated based on the Indonesian anagram are used. Furthermore, for conducting the experiment, 34 participants were asked to type a set of words using the smartphone keyboard. Then, each user’s keystroke is recorded. The keystroke dynamic feature consists of latency and digraph which are extracted from the record. According to the experiment result, the error of the proposed method is decreased by 23.075% of EER with FAR and FRR are decreased by 16.381% and 10.41% respectively, compared with Kim’s method. It means that the proposed method is successful increase the biometrics performance by reducing the error rates

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Ugwunna, C.O., O.E. Chukwuogo, O.A. Alabi, M.K. Kareem, T.S. Belonwu, and S.O. Oloyede. "Improving network security using keyboard dynamics: A comparative study." International Research Journal of Science, Technology, Education, and Management 3, no. 4 (2023): 104–21. https://doi.org/10.5281/zenodo.10516267.

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Duplication or imitation of individual keystroke rhymes is very difficult which can make it very efficient to be used for identity authentication. Over time, it is possible that the keystroke style of an individual to be learned by following keystroke information obtained when the person types text. The user’s identity can always be verified by studying the user’s keyboard input styles anytime the user uses the keyboard. The technique suggested in this study uses the keystrokes that users make while typing to verify their identities. To provide an accurate verification of whether a user is authentic or fraudulent, a model that integrates machine learning and dynamic keystroke models—Decision Tree, Random Forest, Support Vector Machine, and K-nearest Neighbors—is compared and utilized. The keystroke dynamics dataset was gathered from Kaggle and consists of 51 subjects' keyboard dynamics data, which was collected over the course of eight sessions and six months. There are 20400 samples in all in the data. This study assessed the effectiveness of machine learning algorithms with a focus on the keystroke dynamic authentication system. Python is used for the development work, while Jupyter notebook is used as the IDE. The performance of the models for different variables is assessed using the following metrics: accuracy, error equal rate, parameter performance, threshold, training time, and testing time. According to the results, the accuracy of the Random Forest, Support Vector Machine, KNN, and Decision Tree algorithms are, respectively, 98, 97.55, 97.28, and 94.26%. Based on the comparing results, Random Forest outperforms the other models, suggesting that Random Forest can be used as the system model for Keystroke Dynamic authentication.

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Ho, Jiacang, and Dae-Ki Kang. "Sequence Alignment with Dynamic Divisor Generation for Keystroke Dynamics Based User Authentication." Journal of Sensors 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/935986.

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Keystroke dynamics based authentication is one of the prevention mechanisms used to protect one’s account from criminals’ illegal access. In this authentication mechanism, keystroke dynamics are used to capture patterns in a user typing behavior. Sequence alignment is shown to be one of effective algorithms for keystroke dynamics based authentication, by comparing the sequences of keystroke data to detect imposter’s anomalous sequences. In previous research, static divisor has been used for sequence generation from the keystroke data, which is a number used to divide a time difference of keystroke data into an equal-length subinterval. After the division, the subintervals are mapped to alphabet letters to form sequences. One major drawback of this static divisor is that the amount of data for this subinterval generation is often insufficient, which leads to premature termination of subinterval generation and consequently causes inaccurate sequence alignment. To alleviate this problem, we introduce sequence alignment of dynamic divisor (SADD) in this paper. In SADD, we use mean of Horner’s rule technique to generate dynamic divisors and apply them to produce the subintervals with different length. The comparative experimental results with SADD and other existing algorithms indicate that SADD is usually comparable to and often outperforms other existing algorithms.

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Sae-Bae, Napa, and Nasir Memon. "Distinguishability of keystroke dynamic template." PLOS ONE 17, no. 1 (2022): e0261291. http://dx.doi.org/10.1371/journal.pone.0261291.

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When keystroke dynamics are used for authentication, users tend to get different levels of security due to differences in the quality of their templates. This paper addresses this issue by proposing a metric to quantify the quality of keystroke dynamic templates. That is, in behavioral biometric verification, the user’s templates are generally constructed using multiple enrolled samples to capture intra-user variation. This variation is then used to normalize the distance between a set of enrolled samples and a test sample. Then a normalized distance is compared against a predefined threshold value to derive a verification decision. As a result, the coverage area for accepted samples in the original space of vector representation is discrete. Therefore, users with the higher intra-user variation suffer higher false acceptance rates (FAR). This paper proposes a metric that can be used to reflect the verification performance of individual keystroke dynamic templates in terms of FAR. Specifically, the metric is derived from statistical information of user-specific feature variations, and it has a non-decreasing property when a new feature is added to a template. The experiments are performed based on two public keystroke dynamic datasets comprising of two main types of keystroke dynamics: constrained-text and free-text, namely the CMU keystroke dynamics dataset and the Web-Based Benchmark for keystroke dynamics dataset. Experimental results based on multiple classifiers demonstrate that the proposed metric can be a good indicator of the template’s false acceptance rate. Thus, it can be used to enhance the security of the user authentication system based on keystroke dynamics.

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Didih, Rizki Chandranegara, Wibowo Hardianto, and Eko Minarno Agus. "Combined scaled manhattan distance and mean of horner's rules for keystroke dynamic authentication." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 770–75. https://doi.org/10.12928/TELKOMNIKA.v18i2.14815.

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Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner’s Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method.

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Siti, Rahayu Selamat, Teck Guan Teh, and Yusof Robiah. "ENHANCED AUTHENTICATION FOR WEB-BASED SECURITY USING KEYSTROKE DYNAMICS." International Journal of Network Security & Its Applications (IJNSA) 12, no. 4 (2020): 01–16. https://doi.org/10.5281/zenodo.3975709.

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Current password authentication system was proven not secure enough to protect the information from intruders. However, various research has been done and the results show the value of FRR still low and the value of FAR still high. Thus, one of the methods suggests, is enhancing the current system using keystroke dynamics. Keystroke dynamics is a type of biometric authentication that does not require any special hardware, easy to use as the same routine as normal password authentication. Therefore, this research proposed an authentication system using keystroke dynamics to prevent the system from intruders. A system is developed that consist of two parts which are enrolment and verification. Then, a prototype is developed for testing process that consists of 3 main modules, namely Enrolment, Client/Server Connection and, Verification and Retraining. Based on the testing, the system proved that the keystroke dynamic authentication system was able to implement in client/server environment and shows the value of EER is low that indicates it provide a better system authentication. In future, the system can be improved by enhancing the security, performance, and user interface.

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Panfilova, I. E., and N. E. Karpova. "Investigate the impact of user’s state on the quality of authentication by keystroke dynamic." Journal of Physics: Conference Series 2182, no. 1 (2022): 012097. http://dx.doi.org/10.1088/1742-6596/2182/1/012097.

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Abstract One of the most important issues in the field of information security has been and remains the issue a reliable user’s authentication. A special place among the possible authentication methods today is occupied by behavioural biometrics, which have a high degree of reliability. A keystroke dynamic, as a type of behavioural biometrics, is also capable of providing a high level of protection of information systems in the case of correctly selected characteristics. This article shows that external factors, including the psychophysiological state of a person, can also influence the authentication process by keystroke dynamics. In the paper, the state of the user was assessed in the process of collecting a sample of keystroke dynamics and a conclusion was made about the presence of such an influence.

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Mao, Rui, Xiaoyu Wang, and Heming Ji. "ACBM: attention-based CNN and Bi-LSTM model for continuous identity authentication." Journal of Physics: Conference Series 2352, no. 1 (2022): 012005. http://dx.doi.org/10.1088/1742-6596/2352/1/012005.

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With the evolution of network attack methods, implicit continuous identity authentication technology has attracted more and more attention. Among them, keystroke dynamics is widely used because it does not need the assistance of devices other than keyboards. In this paper, we propose a keystroke dynamic identity authentication model based on deep learning. This model combines convolutional neural network (CNN), bi-directional Long Short-Term Memory (BI-LSTM), and the attention mechanism. Unlike most existing models that only use keystroke time as the feature vector, this model uses keystroke content and keystroke time as the feature vector. First, CNN is used to process feature vectors. Then the normalized vector is input into the bi-LSTM network for training. The model in this paper is tested using Buffalo open data set. The results show that FRR (False Reject Rate), FAR (False Accept Rate), and EER(Equal Error Rate) are 3.09%, 3.03%, and 4.23%, respectively. The validity and accuracy of the model in continuous identity authentication are proved.

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Shradha Pandule, Akshada Ringe, Jaid Sayyed, and Prof. S. C. Puranik. "Keystroke Tracking-Robust System with Dual-Keypad Security." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2098–102. https://doi.org/10.47392/irjaeh.2025.0306.

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The development of an advanced authentication mechanism designed to mitigate the increasing risk of keylogging attacks, a prevalent cyber threat that captures keystrokes to steal sensitive information. Traditional authentication methods relying on keyboard input are particularly vulnerable to such attacks. To address this, a novel security framework is introduced, combining two innovative components: a dual-keypad input system and a visual authentication protocol. The dual-keypad system employs two distinct input interfaces—a physical keypad and a virtual keypad—each handling separate aspects of the authentication process. This division significantly complicates the ability of keyloggers to capture complete authentication sequences, thereby enhancing security. Complementing this, the visual authentication component incorporates a dynamic, graphical verification process. Users interact with visual elements, such as images or patterns displayed on a screen, which are inherently resistant to keylogging. This approach not only strengthens security but also ensures a user-friendly experience. Together, these systems form a multi-layered defense strategy. The dual-keypad mechanism minimizes the risk of keystroke compromise, while the visual authentication ensures that even if keystrokes are intercepted, the authentication process remains secure. This research aims to deliver a robust, secure, and intuitive authentication solution that effectively counters keylogging and other cyber threats, offering a reliable method for safeguarding sensitive information across various applications.

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Fouad, Khaled Mohammed, Basma Mohammed Hassan, and Mahmoud F. Hassan. "User Authentication based on Dynamic Keystroke Recognition." International Journal of Ambient Computing and Intelligence 7, no. 2 (2016): 1–32. http://dx.doi.org/10.4018/ijaci.2016070101.

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Biometric identification is a very good candidate technology, which can facilitate a trusted user authentication with minimum constraints on the security of the access point. However, most of the biometric identification techniques require special hardware, thus complicate the access point and make it costly. Keystroke recognition is a biometric identification technique which relies on the user behavior while typing on the keyboard. It is a more secure and does not need any additional hardware to the access point. This paper presents a developed behavioral biometric authentication method which enables to identify the user based on his Keystroke Static Authentication (KSA) and describes an authentication system that explains the ability of keystroke technique to authenticate the user based on his template profile saved in the database. Also, an algorithm based on dynamic keystroke analysis has been presented, synthesized, simulated and implemented on Field Programmable Gate Array (FPGA). The proposed algorithm is tested on 25 individuals, achieving a False Rejection Rate (FRR) about 4% and a False Acceptance Rate (FAR) about 0%. This performance is reached using the same sampling text for all the individuals. In this paper, two methods are used to implement the proposed approach: method one (H/W based Sorter) and method two (S/W based Sorter) are achieved execution time about 50.653 ns and 9.650 ns, respectively. Method two achieved a lower execution time; the time in which the proposed algorithm is executed on FPGA board, compared to some published results. As the second method achieved a small execution time and area utilization so it is the preferred method to be implemented on FPGA.

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Babaeizadeh, Mahnoush, Majid Bakhtiari, and Mohd Aizaini Maarof. "Keystroke Dynamic Authentication in Mobile Cloud Computing." International Journal of Computer Applications 90, no. 1 (2014): 29–36. http://dx.doi.org/10.5120/15541-4274.

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Cockell, Robert, and Basel Halak. "On the Design and Analysis of a Biometric Authentication System Using Keystroke Dynamics." Cryptography 4, no. 2 (2020): 12. http://dx.doi.org/10.3390/cryptography4020012.

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This paper proposes a portable hardware token for user’s authentication; it is based on the use of keystroke dynamics to verify users biometrically. The proposed approach allows for a multifactor authentication scheme, in which a user cannot be granted access unless they provide a correct password on a hardware token and their biometric signature. The latter is extracted while the user is typing their password. This paper explains the design rationale of the proposed system and provides a comprehensive insight in the development of a hardware prototype of the same. The paper also presents a feasibility study that included a systematic analysis based on training data obtained from 32 users. Our results show that dynamic keystroke can be employed to construct a cost-efficient solution for biometric user authentication with an average error rate of 4.5%.

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Anjali, Somwanshi, Karmalkar Devika, Agrawal Sachi, Nanaware Poonam, and Geetanjali Sharma Mrs. "Dynamic Grid Based Authentication With Improved Security." International Journal of Advances In Scientific Research and Engineering (IJASRE) 3, no. 3 (2017): 9–15. https://doi.org/10.5281/zenodo.495718.

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Today IT infrastructure is one of the important parts of everyone’s life. Various applications are used for string managing and transferring data from one place to another. We have various techniques to secure these applications. Textual password is most commonly used authentication technique for securing these applications. Authentication schemes are vulnerable to various types of attacks. The proposed system provides solution to the attacks namely, ‘Keystroke Logging’, ‘Shoulder Surfing’ and ‘Duplicate Login Pages’. The system improves login security mechanism.

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Alotoum, Y. M. A. A., and A. V. Krasov. "Soft Biometrics for Authentication and Identification Hand Based on the Use of the Keyboard." Proceedings of Telecommunication Universities 10, no. 6 (2024): 55–67. https://doi.org/10.31854/1813-324x-2024-10-6-55-67.

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Relevance. Nowadays, technological systems, artificial intelligence, the general availability of the Internet and penetration into the systems of banks, institutions and social networks have become a studied science and are accessible to all groups and ages. One of the main tasks was to provide a system for protecting confidential information from hackers, as well as easy access to authentication and identification of users. Biometric systems came to the fore, including mouse movement dynamics and keystroke dynamics, which reveal the typing style and mouse movement of each person. Soft biometrics is an interesting and inexpensive biometric method that does not require additional equipment. The system identifies a person based on the input information they enter in a special column. Hand identification dynamics falls into the category of behavioral soft biometrics, that is, the user's patterns reflect the individual program of actions that he follows when using the site.The goal of this article the purpose of this work is to improve the security level by creating a function that will strengthen the authentication system and improve the iron gate Методы исследования. In carrying out the work, methods of analysis and synthesis, theories of algorithms, laws of kinematics, neural networks, keystroke dynamics and soft biometrics were used.Results. A method for extracting dynamic characteristics of keystrokes is described. A neural network is created and a threshold value is determined for identifying the type of typing hand.Scientific novelty. Unlike known authentication methods, the proposed method is used to determine the typing hand on the keyboard through a neural network using the laws of kinematics, soft biometrics and extracting the dynamics of keystrokes in order to determine the value and accuracy of determining the type of typing hand.Significance. The proposed solution allows to increase the security of user authentication, increase the speed of implementation and reduce the cost. The results obtained in the work are positive and can be used in the near future. In turn, soft biometric measurements depend on human behavioral patterns, which complicates user falsification. It is difficult to imitate typing behavior, since it is ballistic (semi-autonomous), which makes behavioral information valuable as a soft and sensitive biometric method.

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Tsimperidis, Ioannis, Shahin Rostami, and Vasilios Katos. "Age Detection Through Keystroke Dynamics from User Authentication Failures." International Journal of Digital Crime and Forensics 9, no. 1 (2017): 1–16. http://dx.doi.org/10.4018/ijdcf.2017010101.

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In this paper an incident response approach is proposed for handling detections of authentication failures in systems that employ dynamic biometric authentication and more specifically keystroke user recognition. The main component of the approach is a multi layer perceptron focusing on the age classification of a user. Empirical findings show that the classifier can detect the age of the subject with a probability that is far from the uniform random distribution, making the proposed method suitable for providing supporting yet circumstantial evidence during e-discovery.

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Sonawane, Miss Aarti Raman, and Prof Kumbhar H. V. "Graphical-Based Password Keystroke Dynamic Authentication System for Android Phone." IJARCCE 5, no. 12 (2016): 377–81. http://dx.doi.org/10.17148/ijarcce.2016.51287.

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Yang, Wen Chuan, Rui Li, and Zhi Dong Shang. "Simulation of a Characteristics Identification Algorithm." Advanced Materials Research 945-949 (June 2014): 2306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2306.

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Keystroke rhythm identification, which extracts biometric characteristics through keyboards without additional expensive devices, is a kind of biometric identification technology. The paper proposes a dynamic identity authentication model based on the improved keystroke rhythm algorithm in Rick Joyce model and implement this model in a mobile phone system. The experimental results show that comparing with the original model, the false alarm rate of the improved model decreases a lot in the mobile phone system, and its growth of imposter pass rate is slower than the Rick Joyce models. The improved model is more suitable for small memory systems, and it has better performance in security and dynamic adaptation. This improved model has good application value.

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Huh, Jun Ho, Sungsu Kwag, Iljoo Kim, et al. "On the Long-Term Effects of Continuous Keystroke Authentication." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 2 (2023): 1–32. http://dx.doi.org/10.1145/3596236.

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One of the main challenges in deploying a keystroke dynamics-based continuous authentication scheme on smartphones is ensuring low error rates over time. Unstable false rejection rates (FRRs) would lead to frequent phone locks during long-term use, and deteriorating attack detection rates would jeopardize its security benefits. The fact that it is undesirable to train complex deep learning models directly on smartphones or send private sensor data to servers for training present unique deployment constraints, requiring on-device solutions that can be trained fully on smartphones. To improve authentication accuracy while satisfying such real-world deployment constraints, we propose two novel feature engineering techniques: (1) computation of pair-wise correlations between accelerometer and gyroscope sensor values, and (2) on-device feature extraction technique to compute dynamic time warping (DTW) distance measurements between autoencoder inputs and outputs via transfer-learning. Using those two feature sets in an ensemble blender, we achieved 6.4 percent equal error rate (EER) in a public dataset. In comparison, blending two state-of-the-art solutions achieved 14.1 percent EER in the same test settings. Our real-world dataset evaluation showed increasing FRRs (user frustration) over two months; however, through periodic model retraining, we were able to maintain average FRRs around 2.5 percent while keeping attack detection rates around 89 percent. The proposed solution has been deployed in the latest Samsung Galaxy smartphone series to protect secure workspace through continuous authentication.

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Pandule, Shradha. "Implementation Towards Enhanced Visual Proof of Identity: Keystroke Tracking-Robust System with Dual-Keypad Security." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47627.

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Abstract: - The project focuses on developing an advanced authentication mechanism to counteract the growing threat of keylogging attacks. Keylogging, a type of cyber-attack that captures keystrokes to steal sensitive information, poses a significant risk to traditional authentication methods that rely on keyboard input. This project introduces a novel security approach combining two key innovations: a dual-keypad input system and a visual authentication protocol. The dual-keypad system consists of two separate input keypad (Normal Keypad and Virtual Keypad), each responsible for a different aspect of the authentication process. This separation complicates the ability for keyloggers to capture complete authentication sequences, thereby enhancing security. Simultaneously, the visual authentication component introduces a dynamic, graphical verification process that complements the dual-keypad system. Users interact with visual elements—such as images or patterns—displayed on a screen, which are not susceptible to keylogging. This adds an additional layer of authentication that is both user-friendly and resistant to data capture by malicious software. The integration of these two systems creates a multi-layered defense strategy. The dual-keypad mechanism reduces the risk of compromised keystrokes, while the visual authentication process ensures that even if keystrokes are captured, the authentication remains secure. The project aims to deliver a robust, secure, and intuitive authentication solution that enhances protection against keylogging and other cyber threats, providing a reliable means of securing sensitive information in various applications. Keywords- Keylogging, Visual Authentication, Dual-Keypad System, Cybersecurity, Authentication Protocols, Secure Input Methods, Data Protection, Multi-Factor Authentication, User Authentication, Security Systems, etc

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Aljahdali, Asia Othman, Fursan Thabit, Hanan Aldissi, and Wafaa Nagro. "Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets." Engineering, Technology & Applied Science Research 13, no. 3 (2023): 10906–15. http://dx.doi.org/10.48084/etasr.5841.

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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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Alsuhibany, Suliman A., and Afnan S. Almuqbil. "Analyzing the Effectiveness of Touch Keystroke Dynamic Authentication for the Arabic Language." Wireless Communications and Mobile Computing 2021 (September 10, 2021): 1–15. http://dx.doi.org/10.1155/2021/9963129.

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The keystroke dynamic authentication (KDA) technique was proposed in the literature to develop a more effective authentication technique than traditional methods. KDA analyzes the rhythmic typing of the owner on a keypad or keyboard as a source of verification. In this study, we extend the findings of the system by analyzing the existing literature and validating its effectiveness in Arabic. In particular, we examined the effectiveness of the KDA system in Arabic for touchscreen-based digital devices using two KDA classes: fixed and free text. To this end, a KDA system was developed and applied to a selected device operating on the Android platform, and various classification methods were used to assess the similarity between log-in and enrolment sessions. The developed system was experimentally evaluated. The results showed that using Arabic KDA on touchscreen devices is possible and can enhance security. It attains a higher accuracy with average equal error rates of 0.0% and 0.08% by using the free text and fixed text classes, respectively, implying that free text is more secure than fixed text.

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Chandranegara, Didih Rizki, Hardianto Wibowo, and Agus Eko Minarno. "Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 2 (2020): 770. http://dx.doi.org/10.12928/telkomnika.v18i2.14815.

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YANG, Wenchuan, and Fang FANG. "Application of a Dynamic Identity Authentication Model Based on an Improved Keystroke Rhythm Algorithm." International Journal of Communications, Network and System Sciences 02, no. 08 (2009): 714–19. http://dx.doi.org/10.4236/ijcns.2009.28082.

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Panfilova, I. Е., and N. E. Karpova. "INVESTIGATE THE IMPACT OF USER’S STATE ON THE QUALITY OF AUTHENTICATION BY KEYSTROKE DYNAMIC." Dynamics of Systems, Mechanisms and Machines 9, no. 4 (2021): 068–74. http://dx.doi.org/10.25206/2310-9793-9-4-68-74.

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Одним из наиболее важных вопросов в области информационной безопасности был и остается вопрос достоверной аутентификации пользователя. Особое место среди возможных методов аутентификации сегодня занимает поведенческая биометрия, обладающая высокой степенью надежности. Клавиатурный почерк, как вид поведенческой биометрии также способен обеспечить высокий уровень защиты информационных систем в случае правильно подобранных характеристик почерка. В данной статье показано, что на процесс аутентификации по клавиатурному почерку также способны оказывать влияние внешние факторы, в числе которых находятся психофизиологическое состояние человека. В работе была произведена оценка состояния пользователя на процесс сбора образца клавиатурного почерка и сделан вывод о наличии такого влияния.

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Chang, Ting-Yi, Cheng-Jung Tsai, and Jyun-Hao Lin. "A graphical-based password keystroke dynamic authentication system for touch screen handheld mobile devices." Journal of Systems and Software 85, no. 5 (2012): 1157–65. http://dx.doi.org/10.1016/j.jss.2011.12.044.

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YEZHOVA, Y. "MULTIMODAL NEURAL NETWORK USER AUTHENTICATION SYSTEMS BASED ON BIOMETRIC FEATURES." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 1, no. 40 (2025): 40–50. https://doi.org/10.31474/1996-1588-2025-1-40-40-50.

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"This paper explores the application of neural networks in multimodal biometric authentication systems, emphasizing the integration of multiple biometric modalities to enhance security, accuracy, and robustness against various attacks. Traditional authentication methods, such as passwords and single-modal biometrics, often suffer from vulnerabilities, including spoofing, environmental factors, and data breaches. To address these challenges, multimodal authentication systems combine several biometric traits, such as facial recognition, fingerprint scanning, voice recognition, and keystroke dynamics, to achieve higher reliability and resistance to security threats. The study provides an overview of public datasets used for training neural network-based biometric authentication models, including VoxCeleb, RAVDESS, MOBIO, and SDUMLA-HMT. These datasets contain diverse biometric information necessary for developing robust multimodal authentication systems. The paper evaluates the effectiveness of existing approaches using key performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and area under the curve (AUC). Additionally, specialized metrics are considered, including failure to enroll rate (FTE), failure to acquire rate (FTA), and template stability (TS), which are crucial for real-world applications. The role of neural networks in multimodal biometric authentication is analyzed by examining state-of-the-art architectures, including convolutional neural networks (CNNs) and deep learning-based feature fusion methods. Various fusion levels—feature-level, score-level, and decision-level—are discussed to determine the optimal integration strategy for improving authentication performance. The results indicate that multimodal systems significantly outperform unimodal authentication methods by reducing vulnerability to spoofing and environmental variations. Experimental findings suggest that integrating multiple biometric traits enhances the system’s adaptability to dynamic conditions, reducing both false acceptance and false rejection rates. Despite these advantages, several challenges remain, including computational complexity, data privacy concerns, and the need for real-time processing capabilities. Future research should focus on optimizing multimodal fusion techniques, improving generalization across different datasets, and enhancing the security of biometric templates against adversarial attacks. Additionally, developing lightweight neural network architectures suitable for mobile and embedded systems is essential for the practical deployment of multimodal authentication technologies"

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Lee, Hyungu, Jung Yeon Hwang, Dong In Kim, Shincheol Lee, Sung-Hoon Lee, and Ji Sun Shin. "Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors." Security and Communication Networks 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2567463.

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Personal Identification Numbers (PINs) and pattern drawing have been used as common authentication methods especially on smartphones. Such methods, however, are very vulnerable to the shoulder surfing attack. Thus, keystroke dynamics that authenticate legitimate users based on their typing manner have been studied for years. However, many of the studies have focused on PC keyboard keystrokes. More studies on mobile and smartphones keystroke dynamics are warranted; as smartphones make progress in both hardware and software, features from smartphones have been diversified. In this paper, using various features including keystroke data such as time interval and motion data such as accelerometers and rotation values, we evaluate features with motion data and without motion data. We also compare 5 formulas for motion data, respectively. We also demonstrate that opposite gender match between a legitimate user and impostors has influence on authenticating by our experiment results.

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Sari, Zamah, Didih Rizki Chandranegara, Rahayu Nurul Khasanah, Hardianto Wibowo, and Wildan Suharso. "Analysis of the Combination of Naïve Bayes and MHR (Mean of Horner’s Rule) for Classification of Keystroke Dynamic Authentication." Jurnal Online Informatika 7, no. 1 (2022): 62. http://dx.doi.org/10.15575/join.v7i1.839.

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Keystroke Dynamics Authentication (KDA) is a technique used to recognize somebody dependent on typing pattern or typing rhythm in a system. Everyone's typing behavior is considered unique. One of the numerous approaches to secure private information is by utilizing a password. The development of technology is trailed by the human requirement for security concerning information and protection since hacker ability of information burglary has gotten further developed (hack the password). So that hackers can use this information for their benefit and can disadvantage others. Hence, for better security, for example, fingerprint, retina scan, et cetera are enthusiastically suggested. But these techniques are considered costly. The advantage of KDA is the user would not realize that the system is using KDA. Accordingly, we proposed the combination of Naïve Bayes and MHR (Mean of Horner’s Rule) to classify the individual as an attacker or a non-attacker. We use Naïve Bayes because it is better for classification and simple to implement than another. Furthermore, MHR is better for KDA if combined with the classification method which is based on previous research. This research showed that False Acceptance Rate (FAR) and Accuracy are improving than the previous research.

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G. Ismail, Mahmoud, Mohammed A. M. Salem, Mohamed A. Abd El Ghany, Eman Abdullah Aldakheel, and Safia Abbas. "Outlier detection for keystroke biometric user authentication." PeerJ Computer Science 10 (June 17, 2024): e2086. http://dx.doi.org/10.7717/peerj-cs.2086.

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User authentication is a fundamental aspect of information security, requiring robust measures against identity fraud and data breaches. In the domain of keystroke dynamics research, a significant challenge lies in the reliance on imposter datasets, particularly evident in real-world scenarios where obtaining authentic imposter data is exceedingly difficult. This article presents a novel approach to keystroke dynamics-based authentication, utilizing unsupervised outlier detection techniques, notably exemplified by the histogram-based outlier score (HBOS), eliminating the necessity for imposter samples. A comprehensive evaluation, comparing HBOS with 15 alternative outlier detection methods, highlights its superior performance. This departure from traditional dependence on imposter datasets signifies a substantial advancement in keystroke dynamics research. Key innovations include the introduction of an alternative outlier detection paradigm with HBOS, increased practical applicability by reducing reliance on extensive imposter data, resolution of real-world challenges in simulating fraudulent keystrokes, and addressing critical gaps in existing authentication methodologies. Rigorous testing on Carnegie Mellon University’s (CMU) keystroke biometrics dataset validates the effectiveness of the proposed approach, yielding an impressive equal error rate (EER) of 5.97%, a notable area under the ROC curve of 97.79%, and a robust accuracy (ACC) of 89.23%. This article represents a significant advancement in keystroke dynamics-based authentication, offering a reliable and efficient solution characterized by substantial improvements in accuracy and practical applicability.

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Namisha Bhasin. "Authentication using Dynamics Keystrokes and Quantum Machine Learning." Journal of Information Systems Engineering and Management 10, no. 49s (2025): 1273–92. https://doi.org/10.52783/jisem.v10i49s.10134.

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Authenticating a user based on his/her typing pattern is known as the keystroke dynamics. Here, Authentication is based on user typing data and user typing data cannot be copy by anyone including machine. Authenticating a user at the time of login is called static keystroke dynamics, whereas authenticating a user after login is called free text authentication. To date, free-text/dynamics/continuous authentication statistical and classical machine learning algorithms have been used. However, in this study, we solve the problem of authentication using classical, and quantum algorithms. The given dataset contained two types of information 1) text and 2) typing rhythm. We use typing rhythm data to solve the authentication problem. Out of classical, and quantum machine learning algorithms, the best performance was achieved by the QSVM algorithms. QSVM is able to solve with 100% accuracy and 0% EER. Among classical fusion of MLP and RF is able to solve the problem with 99.3% accuracy and EER of 0.007%.

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Tang, Benxiao, Zhibo Wang, Run Wang, Lei Zhao, and Lina Wang. "Niffler: A Context-Aware and User-Independent Side-Channel Attack System for Password Inference." Wireless Communications and Mobile Computing 2018 (2018): 1–19. http://dx.doi.org/10.1155/2018/4627108.

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Digital password lock has been commonly used on mobile devices as the primary authentication method. Researches have demonstrated that sensors embedded on mobile devices can be employed to infer the password. However, existing works focus on either each single keystroke inference or entire password sequence inference, which are user-dependent and require huge efforts to collect the ground truth training data. In this paper, we design a novel side-channel attack system, called Niffler, which leverages the user-independent features of movements of tapping consecutive buttons to infer unlocking passwords on smartphones. We extract angle features to reflect the changing trends and build a multicategory classifier combining the dynamic time warping algorithm to infer the probability of each movement. We further use the Markov model to model the unlocking process and use the sequences with the highest probabilities as the attack candidates. Moreover, the sensor readings of successful attacks will be further fed back to continually improve the accuracy of the classifier. In our experiments, 100,000 samples collected from 25 participants are used to evaluate the performance of Niffler. The results show that Niffler achieves 70% and 85% accuracy with 10 attempts in user-independent and user-dependent environments with few training samples, respectively.

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Tsimperidis, Ioannis, Olga-Dimitra Asvesta, Eleni Vrochidou, and George A. Papakostas. "IKDD: A Keystroke Dynamics Dataset for User Classification." Information 15, no. 9 (2024): 511. http://dx.doi.org/10.3390/info15090511.

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Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state of users for purposes that serve human–computer interaction. Studies of keystroke dynamics have used datasets created from volunteers recording fixed-text typing or free-text typing. Unfortunately, there are not enough keystroke dynamics datasets available on the Internet, especially from the free-text category, because they contain sensitive and personal information from the volunteers. In this work, a free-text dataset is presented, which consists of 533 logfiles, each of which contains data from 3500 keystrokes, coming from 164 volunteers. Specifically, the software developed to record user typing is described, the demographics of the volunteers who participated are given, the structure of the dataset is analyzed, and the experiments performed on the dataset justify its utility.

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El-Kenawy, El-Sayed M., Seyedali Mirjalili, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Nima Khodadadi, and Marwa M. Eid. "Meta-Heuristic Optimization and Keystroke Dynamics for Authentication of Smartphone Users." Mathematics 10, no. 16 (2022): 2912. http://dx.doi.org/10.3390/math10162912.

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Personal Identification Numbers (PIN) and unlock patterns are two of the most often used smartphone authentication mechanisms. Because PINs have just four or six characters, they are subject to shoulder-surfing attacks and are not as secure as other authentication techniques. Biometric authentication methods, such as fingerprint, face, or iris, are now being studied in a variety of ways. The security of such biometric authentication is based on PIN-based authentication as a backup when the maximum defined number of authentication failures is surpassed during the authentication process. Keystroke-dynamics-based authentication has been studied to circumvent this limitation, in which users were categorized by evaluating their typing patterns as they input their PIN. A broad variety of approaches have been proposed to improve the capacity of PIN entry systems to discriminate between normal and abnormal users based on a user’s typing pattern. To improve the accuracy of user discrimination using keystroke dynamics, we propose a novel approach for improving the parameters of a Bidirectional Recurrent Neural Network (BRNN) used in classifying users’ keystrokes. The proposed approach is based on a significant modification to the Dipper Throated Optimization (DTO) algorithm by employing three search leaders to improve the exploration process of the optimization algorithm. To assess the effectiveness of the proposed approach, two datasets containing keystroke dynamics were included in the conducted experiments. In addition, we propose a feature selection algorithm for selecting the proper features that enable better user classification. The proposed algorithms are compared to other optimization methods in the literature, and the results showed the superiority of the proposed algorithms. Moreover, a statistical analysis is performed to measure the stability and significance of the proposed methods, and the results confirmed the expected findings. The best classification accuracy achieved by the proposed optimized BRNN is 99.02% and 99.32% for the two datasets.

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BASHKOV, Y., T. ALTUKHOVA, and Y. YEZHOVA. "DEVELOPMENT OF A USER AUTHENTICATION METHOD BASED ON KEYBOARD HANDWRITING." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 2 - №1, no. 35-36 (2023): 61–68. http://dx.doi.org/10.31474/1996-1588-2023-1-36-61-68.

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"In this research paper, a study of user authentication by keyboard handwriting when entering a passphrase is performed. Based on the analysis and development of a mathematical function for the distribution of ""own"" and ""foreign"" areas, a module for filtering the author's input was created. To authenticate a user by keyboard handwriting when entering a passphrase, it is necessary to recognize the speed and dynamics of input (gaps between keystrokes and their retention). The time intervals between keystrokes and the period of keystroke hold allow us to characterize the user's handwriting on the keyboard quite unambiguously, which is confirmed by a number of experiments conducted during the study of user authentication features. In addition, the authentication method based on keyboard handwriting can be used to protect against fraudsters trying to gain unauthorized access to the system and for remote authentication when users are at a distance from the server. The results of the study and the developed software module can be used to create a hybrid access control system that combines two authentication methods - password and biometric. Thus, the final control system will provide an enhanced authentication procedure compared to classical password authentication. The keyboard handwriting authentication method has great potential for use in the field of cybersecurity and can be used as an effective tool to ensure system security. "

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Hassan, Basma Mohammed, Khaled Mohammed Fouad, and Mahmoud Fathy Hassan. "Keystroke Dynamics Authentication in Cloud Computing." International Journal of Enterprise Information Systems 11, no. 4 (2015): 99–120. http://dx.doi.org/10.4018/ijeis.2015100105.

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Cloud computing needs a strong and efficient authentication system because the user will access his rented part through a faraway connection and it will make the authentication sensor device besides the user place for identification and verification so how to know the user who claimed himself to be the legal user. Keystroke identification system as a biometric authentication technique is strongly Candidate for the security issues in cloud computing technology. Keystroke dynamics as a security system did not need extra hardware because the authentication device will be the existing keyboard based on everyone has a unique style for writing. The other biometric methods are addressed with each advantage and disadvantage along with keystroke method. In this paper, all known studies about keystroke technique are explained and compared between them according to the classification technique, number of the participated users and each study results then introduces a survey on software and hardware of other biometric authentication techniques and after the literature review is addressed then keystroke as a biometric authentication system is suggested to access cloud computing environment because it has many advantages to being a part of the known security systems which spread in our world.

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Gawade, Sujata Prashant. "Review Paper on Keystroke Dynamics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39908.

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In Today’s Digital world need of authentication is not limited to password and PIN. It requires a high level of security which can be achieved by Keystroke biometrics. This review paper attempts to catch the imposter even if one carries login details of genuine user. The paper tries to review the keystroke methods and draw a common conclusion. Adding keystroke mechanism with existing authentication system helps to enhance the security. Keywords: Keystroke Biometrics, Network Security, Password Security and Password Strengthening.

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Andrean, Alvin, Manoj Jayabalan, and Vinesh Thiruchelvam. "Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron." International Journal of Machine Learning and Computing 10, no. 1 (2020): 134–39. http://dx.doi.org/10.18178/ijmlc.2020.10.1.910.

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

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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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KUMAR, B. VINOTH. "KEYLOGGER - INNOVATIVE KEYSTROKE DYNAMICS AND ENSURING SECURE AUTHENTICATION." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02956.

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In the digital era, the need for secure and reliable user authentication mechanisms has become more critical than ever. Traditional password-based authentication systems are increasingly vulnerable to threats such as keylogging, phishing, and brute-force attacks. To address these issues, keystroke dynamics has emerged as an innovative and promising solution. Keystroke dynamics is a behavioral biometric technique that analyzes the unique typing patterns of individuals. Every person has a distinct way of typing, characterized by the speed of key presses, the time interval between each keystroke, and the overall typing rhythm. These patterns are difficult to replicate, making keystroke dynamics a powerful tool for enhancing security.

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Rajput, Pulkit. "Secure Authentication with Keystroke Dynamics." Global Sci-Tech 12, no. 3 (2020): 133–40. http://dx.doi.org/10.5958/2455-7110.2020.00016.6.

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Bergadano, Francesco, Daniele Gunetti, and Claudia Picardi. "User authentication through keystroke dynamics." ACM Transactions on Information and System Security 5, no. 4 (2002): 367–97. http://dx.doi.org/10.1145/581271.581272.

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Choi, Maro, Shincheol Lee, Minjae Jo, and Ji Sun Shin. "Keystroke Dynamics-Based Authentication Using Unique Keypad." Sensors 21, no. 6 (2021): 2242. http://dx.doi.org/10.3390/s21062242.

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Authentication methods using personal identification number (PIN) and unlock patterns are widely used in smartphone user authentication. However, these authentication methods are vulnerable to shoulder-surfing attacks, and PIN authentication, in particular, is poor in terms of security because PINs are short in length with just four to six digits. A wide range of research is currently underway to examine various biometric authentication methods, for example, using the user’s face, fingerprint, or iris information. However, such authentication methods provide PIN-based authentication as a type of backup authentication to prepare for when the maximum set number of authentication failures is exceeded during the authentication process such that the security of biometric authentication equates to the security of PIN-based authentication. In order to overcome this limitation, research has been conducted on keystroke dynamics-based authentication, where users are classified by analyzing their typing patterns while they are entering their PIN. As a result, a wide range of methods for improving the ability to distinguish the normal user from abnormal ones have been proposed, using the typing patterns captured during the user’s PIN input. In this paper, we propose unique keypads that are assigned to and used by only normal users of smartphones to improve the user classification performance capabilities of existing keypads. The proposed keypads are formed by randomly generated numbers based on the Mersenne Twister algorithm. In an attempt to demonstrate the superior classification performance of the proposed unique keypad compared to existing keypads, all tests except for the keypad type were conducted under the same conditions in earlier work, including collection-related features and feature selection methods. Our experimental results show that when the filtering rates are 10%, 20%, 30%, 40%, and 50%, the corresponding equal error rates (EERs) for the proposed keypads are improved by 4.15%, 3.11%, 2.77%, 3.37% and 3.53% on average compared to the classification performance outcomes in earlier work.

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Vaishnav, Pragya, Manju Kaushik, and Linesh Raja. "Behavioral biometric authentication on smartphone using keystroke dynamics." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 2 (2023): 591–600. http://dx.doi.org/10.47974/jdmsc-1645.

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Over the past few years, smartphones have revolutionized our life. Authentication plays important role as all private and business data are stored in smartphone. Password authentication is not secure technique as password can be easily stolen by hackers. Hence, need to use advance technology to increase the security of authentication. Keystroke Dynamics is behavioral based authentication technique. It identifies the user identity through their typing behavior to check user is genuine or imposter. In this paper author is presenting a KDSP system for Smart devices to increase the security of authentication at login time. On the basis of timing feature set, typing speed, flight time and error rate of keystroke dynamics author is checking the identity of the user. Proposed system works on all the Android smartphones and tablets. All experimental work was performed on 268 subjects to capture keystroke data on server. In Statistical analysis, Equal-error-rate (EER) is measured 7.415% of the 60 subjects for False acceptance rate (FAR) and 208 subjects for False rejection rate (FRR).

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Wangsuk, Kasem, and Tanapat Anusas-amornkul. "Trajectory Mining for Keystroke Dynamics Authentication." Procedia Computer Science 24 (2013): 175–83. http://dx.doi.org/10.1016/j.procs.2013.10.041.

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Teh, Pin Shen, Andrew Beng Jin Teoh, Connie Tee, and Thian Song Ong. "Keystroke dynamics in password authentication enhancement." Expert Systems with Applications 37, no. 12 (2010): 8618–27. http://dx.doi.org/10.1016/j.eswa.2010.06.097.

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Zareen, Farhana Javed, Chirag Matta, Akshay Arora, Sarmod Singh, and Suraiya Jabin. "An authentication system using keystroke dynamics." International Journal of Biometrics 10, no. 1 (2018): 65. http://dx.doi.org/10.1504/ijbm.2018.090129.

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Jabin, Suraiya, Sarmod Singh, Akshay Arora, Farhana Javed Zareen, and Chirag Matta. "An authentication system using keystroke dynamics." International Journal of Biometrics 10, no. 1 (2018): 65. http://dx.doi.org/10.1504/ijbm.2018.10011201.

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Das, Rajat Kumar, Sudipta Mukhopadhyay, and Puranjoy Bhattacharya. "User Authentication Based on Keystroke Dynamics." IETE Journal of Research 60, no. 3 (2014): 229–39. http://dx.doi.org/10.1080/03772063.2014.914686.

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Altwaijry, Najwa. "Authentication by Keystroke Dynamics: The Influence of Typing Language." Applied Sciences 13, no. 20 (2023): 11478. http://dx.doi.org/10.3390/app132011478.

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Keystroke dynamics is a biometric method that uses a subject’s typing patterns for authentication or identification. In this paper we investigate typing language as a factor influencing an individual’s keystroke dynamics. Specifically, we discern whether keystroke dynamics is contingent on the spatial arrangement of letters on the keyboard, or alternatively, whether it is influenced by the linguistic characteristics inherent to the language being used. For this purpose, we construct a new dataset called the Bilingual Keystroke Dynamics Dataset in two languages: English and Arabic. The results show that the authentication system is not contingent on the spatial arrangement of the letters, and is primarily influenced by the language being used, and a system that is used by bilingual users must take into account that each user should have two profiles created, one for each language. An average equal error rate of 0.486% was achieved when enrolling in English and testing on Arabic, and 0.475% when enrolling in Arabic and testing on English.

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Grunova, Denitsa, and Ioannis Tsimperidis. "Finding the Age and Education Level of Bulgarian-Speaking Internet Users Using Keystroke Dynamics." Eng 4, no. 4 (2023): 2711–21. http://dx.doi.org/10.3390/eng4040154.

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The rapid development of information and communication technologies and the widespread use of the Internet has made it imperative to implement advanced user authentication methods based on the analysis of behavioural biometric data. In contrast to traditional authentication techniques, such as the simple use of passwords, these new methods face the challenge of authenticating users at more complex levels, even after the initial verification. This is particularly important as it helps to address risks such as the possibility of forgery and the disclosure of personal information to unauthorised individuals. In this study, the use of keystroke dynamics has been chosen as a biometric, which is the way a user uses the keyboard. Specifically, a number of Bulgarian-speaking users have been recorded during their daily keyboard use, and then a system has been implemented which, with the help of machine learning models, recognises certain acquired or intrinsic characteristics in order to reveal part of their identity. The results show that users can be categorised using keystroke dynamics, in terms of the age group they belong to and in terms of their educational level, with high accuracy rates, which is a strong indication for the creation of applications to enhance user security and facilitate their use of Internet services.

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