Typing Rhythm Research Articles

Behavioural Biometrics as a User Authentication Mechanism in ISMS

Securing sensitive data and mitigating cyber threats necessitates robust user authentication mechanisms within Information Security Management Systems (ISMS). Conventional authentication approaches, such as passwords and static biometric identifiers, exhibit vulnerabilities, urging the adoption of more sophisticated solutions. Behavioral biometrics, which analyzes unique behavioral patterns like typing rhythm and mouse movements, offers a promising avenue for enhancing authentication security while optimizing user experience. This paper examines the integration of behavioral biometrics into ISMS for user verification, emphasizing its advantages including continuous, multimodal, risk-based, and adaptive authentication. Additionally, it addresses considerations like data privacy, regulatory compliance, user experience enhancement, and monitoring and analysis to ensure successful implementation and efficacy. By leveraging behavioral biometrics as part of a holistic authentication strategy, organizations can fortify their security posture, mitigate threats, and adapt to evolving cybersecurity challenges.

The effect of socially evaluated multitasking stress on typing rhythms.

Individuals have unique typing rhythms characterized by specific keystroke dynamics. Changes in state and cardiovascular responding are well documented manifestations of the fight-flight response to stress. However, as stress also leads to changes in muscle tone and motor control, typing rhythms may also be impacted. We aim to determine which individuals are experiencing stress through their typing rhythms and identify universal keystroke markers of stress. Participants (N= 116) typed 80 repetitions of a 6-word, 30-character phrase before and after 15 min of critically evaluated multitasking stress. Cardiovascular, hemodynamic, and state variables were compared across baseline, stress, and recovery periods and measures of typing rhythm were derived for each period and classified using machine-learning algorithms. Critically evaluated multitasking led to significant changes in all stress measures, demonstrating highly robust stress reactivity. Machine learning algorithms accurately classified stressed typing for each individual based on their typing rhythms; however, no universal keystroke markers of stress were identified. Using typing rhythms. We were able to determine whether an individual was stressed or not, but the markers used for classification differed between individuals. These individual changes may provide opportunities for identifying stressful periods through keystroke monitoring, as well as the potential for early identification of disorders which may impact fine motor control. Typing rhythms could therefore be used to monitor health and well-being in individuals who use keyboards in various situations. This is the first rigorous assessment of stress and typing rhythms and has led to the development of a feasible and highly reproducible research protocol.

Analysis of the Combination of Naïve Bayes and MHR (Mean of Horner’s Rule) for Classification of Keystroke Dynamic Authentication

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.

Keystroke-Based Biometric Authentication in Mobile Applications

Keystroke dynamics is the study of how people can be distinguished based on their typing rhythms. It is considered as a real alternative to several authentication mechanisms. In order to improve authentication security in mobile applications, especially mobile chat applications , we propose in this work an approach of user keystroke-based authentication which is based on two important mechanisms, firstly the authentication is done on the basis of a supervised classification model trained on the dynamic keystroke database (android platform) the prediction accuracy has reached high values. When the attacker used the same behavior as the legitimate user we are strengthening security by another authentication mechanism based on three parameters during the opening of a communication session between two users (the number of messages per session, connection time and inter-click time). The proposed system has shown satisfactory results by combining user behaviours and connection parameters during an open session between two users to reinforce the authentication aspect.

Keystroke Dynamics-Based Authentication System Using Empirical Thresholding Algorithm

In a password-based authentication technique, whenever the typed password and username matches the system database, the secure login page allows the client to access it. Despite the password matching, the proposed method checks the similarity between the typing rhythm of entered password and the rhythm of password samples in client's database. In this paper, a novel algorithmic procedure is presented to authenticate the legal client based on empirical threshold values obtained from the timing information of the client's keystroke dynamics. The exploratory outcomes demonstrate an impressive diminish in both false rejection rate and false acceptance rate. Equal error rate and authentication accuracy are also assessed to show the superiority and robustness of the method. Therefore, the proposed keystroke dynamics-based authentication method can be valuable in securing the system protection as a correlative or substitute form of client validation and as a useful resource for identifying the illegal invasion.

Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence.

Cyberattack is one of the severe threats in the digital world as it encompasses everything related to personal information, health, finances, intellectual properties, and even national security. Password‐based authentication is the most practiced authentication system, however, is vulnerable to several attacks such as dictionary attack, shoulder surfing attack, and guessing attack. Here, a new keystroke dynamics‐based hybrid nanogenerator for biometric authentication and identification integrated with artificial intelligence (AI) is reported. Keystroke dynamics offer behavioral and contextual information that can distinguish and authorize the individuals based on their typing rhythms. The hybrid electromagnetic‐triboelectric nanogenerators/sensors efficiently convert the keystroke mechanical energy into electrical signals, which are fed into an artificial neural network based AI system. The self‐powered hybrid sensors‐based biometric authentication system integrated with a neural network achieves an accuracy of 99% and offers a promising hybrid security layer against password vulnerability.

Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron

User authentication is an essential factor to protect digital service and prevent malicious users from gaining access to the system. As Single Factor Authentication (SFA) is less secure, organizations started to utilize Multi-Factor Authentication (MFA) to provide reliable protection by using two or more identification measures. Keystroke dynamics is a behavioral biometric, which analyses users typing rhythm to identify the legitimacy of the subject accessing the system. Keystroke dynamics that have a low implementation cost and does not require additional hardware in the authentication process since the collection of typing data is relatively simple as it does not require extra effort from the user. This study aims to propose deep learning model using Multilayer Perceptron (MLP) in keystroke dynamics for user authentication on CMU benchmark dataset. The user typing rhythm from 51 subjects collected based on the static password (.tie5Roanl) typed 400 times over 8 sessions and 50 repetitions per session. The MLP achieved optimum EER of 4.45% compared to original benchmark classifiers such as 9.6% (scaled Manhattan), 9.96% (Mahalanobis Nearest Neighbor), 10.22% (Outlier Count), 10.25% and 16.14% (Neural Network Auto-Assoc). © 2020 by the authors.

Double serial adaptation mechanism for keystroke dynamics authentication based on a single password

Cyber-attacks have spread all over the world to steal information such as trade secrets, intellectual property and banking data. Facing the danger of the insecurity of saved data (personal, professional, official, etc.), keystroke dynamics was proposed as an interesting, non-intrusive, inexpensive, permanent and weakly constrained solution for users. Based on the typing rhythm of users, it improves logical access security. Nevertheless, it was demonstrated that such an authentication mechanism would need a larger number of samples to enroll the typing characteristics of users. Moreover, these registered characteristics generally undergo aging effects after a time span. Different solutions have been suggested to remedy these variability problems, including template adaptation. In this paper, we propose a double serial adaptation strategy that considers a single-capture-based enrollment process. When using the authentication system, the template of users and the decision/adaptation thresholds are updated. Experimental results on three public keystroke dynamics datasets show the benefits of the proposed method.

Performance evaluation of an anomaly-detection algorithm for keystroke-typing based insider detection

Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user's legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by F1-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an F1-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.

Game Utilization as a Media to Train the Balance of Left and Right Brain

Human have two brain hemispheres, left hemisphere and right hemisphere. Left hemisphere is used for processing language, words, numbers, equations, etc. Right hemisphere is used for processing creativity, imagination, music, color, etc. Every human should have balance between left and right hemisphere. One method that could be used for balancing brain hemispheres is to use left and right hands for using tools, writing, or typing. “Typing Rhythm” is a game for PC platform, the purpose of this game is for brain balancing exercise by typing lyric of a song while the song is played.

Literature Review on Fingerprint Level 1 and Level 2 Features Enhancement to Improve Quality of Image

Biometrics is the one most popular property in human distinguishing proof based on physical or behavioral features. The different physiological characteristics are Fingerprint, DNA, Face, hand, retina, ear features, and odor, where as behavioral characteristics or features are typing rhythm, gait, gesture, and voice with the basic premise that all are unique and all human beings are identified by these intrinsic traits. In the physiological traits, Fingerprint is most commonly utilized the biometric feature in diverse fields for identification and verification purpose. Fingerprint features can be separated into three noteworthy classifications in view of the granularity at which they are removed as level 1, level 2, and level 3 features. Level 1 feature contains macro details, which are easily extractable and include orientation filed, ridge frequency filed and pattern configuration. Only these global features or Level 1 features are not sufficient to uniquely identify or recognize, but if these features are used along with level 2 or level 3 features, that can make the fingerprint recognition system more robust and secure. Level 1 features are used for image enhancement and orientation purpose. In this paper, we made a survey of existing literature on Level 1 features and try to analyze other researcher's contribution to this field.

Literature Review on Fingerprint Level 1 and Level 2 Features Enhancement to Improve Quality of Image

Biometrics is the one most popular property in human distinguishing proof based on physical or behavioral features. The different physiological characteristics are Fingerprint, DNA, Face, hand, retina, ear features, and odor, where as behavioral characteristics or features are typing rhythm, gait, gesture, and voice with the basic premise that all are unique and all human beings are identified by these intrinsic traits. In the physiological traits, Fingerprint is most commonly utilized the biometric feature in diverse fields for identification and verification purpose. Fingerprint features can be separated into three noteworthy classifications in view of the granularity at which they are removed as level 1, level 2, and level 3 features. Level 1 feature contains macro details, which are easily extractable and include orientation filed, ridge frequency filed and pattern configuration. Only these global features or Level 1 features are not sufficient to uniquely identify or recognize, but if these features are used along with level 2 or level 3 features, that can make the fingerprint recognition system more robust and secure. Level1 features are used for image enhancement and orientation purpose. In this paper, we made a survey of existing literature on Level 1 features and try to analyze other researcher's contribution to this field.

Use of keyboard handwriting in systems of the user authentication

Questions of biometric characteristics using for increase efficiency of user authentication are briefly considered in article. The identifier using biometric characteristics is inseparably linked with the user, and it is illegally almost impossible to use it. As biometric characteristics appropriate to use the keyboard handwriting. Keyboard handwriting, or a text typing rhythm, displays a text typing method on the keyboard, inherent only to the specific user. Furthermore it is quite simple to implement and requires no additional hardware costs. As a key text it is advisable to use a password. Moreover, the use of handwriting keyboard when entering your password eliminates the main disadvantages of the classic password systems and systems based on access cards. The description of the developed program for practical use of keyboard handwriting is given. The program except decision making allows to make collecting of statistical material for the subsequent analysis. Use of the program does not cause inconveniences to the user. The program provides maintenance log, which records and stores the date and time of login, username, the number of errors made when entering the password, time and authorization result. The magazine is used for the monitoring of user access. The statistical data obtained by means of the developed program will allow to make number of estimates: informational content of separate characteristics of keyboard handwriting and their different sets, stability in time of characteristics of keyboard handwriting, influence of physical and emotional status of the user on characteristics of his handwriting and others.

Keystroke Dynamics for User Authentication and Identification by using Typing Rhythm

In this era computer security is an important issue now a days because these are used everywhere to store & process the sensitive data. Specially those used in e-banking, e-commerce, virtual offices, e-learning, distributed, computing & various services over the internet. Using Keystroke dynamics authentication technology can be secured by password from various attacks. This technique is based on human behavior to type their password. Here analysis is done using human behavior with their typing pattern. As keystroke dynamics does not require any hardware, no extra hardware is used. Only software based technology is required for password protection. The result provides emphasis with pleasure security that growing in demand in web-based application.

Enhanced template update: Application to keystroke dynamics

With the increasing number of activities being performed using computers, there is an ever growing need for advanced authentication mechanisms like biometrics. One efficient and low cost biometric modality is keystroke dynamics, which attempts to recognize users by their typing rhythm. It has been shown that the biometric features may undergo changes over time, which can reduce the predictive performance of the biometric system. Template update adapts the user model to deal with these changes and, therefore, decreases the predictive performance loss. Most of the studies in the literature only take into account samples classified as genuine to perform adaptation. This paper extends this common approach by proposing an original framework to make use of samples classified as impostors, too. This new approach, named Enhanced Template Update, uses all collected unlabeled samples to support the adaptation process. According to our experimental results, this new approach can improve the predictive performance when compared to current methods depending on the scenario. Some improvements on the visualization of results over time are also proposed during the analysis performed in this study. Although the proposed approach is evaluated on keystroke dynamics, it could also be applied to other biometric modalities.

Keystroke Dynamics for Mobile Phones: A Survey

Biometric is the science of authenticating a user based on his physical or behavioral attributes. Keystroke dynamics is behavioral study which analyses the typing rhythm of the user. We adopted a systematic procedure for studying the state of the art in keystroke dynamics in mobile phones. We analyzed the features extracted, the classification techniques, the input text, length of the input text, number of users, hardware used and the results that each study got. We included research articles that focused on keystroke dynamics for mobile devices only. It was found that majority of the research used latency as the prominent feature. Hold time and pressure are also used in combination with latency to get improved results. The most popular classification techniques are either statistical or neural network based, although it is difficult to say which is better since the users, testing conditions and features used are different in all researches. Also the number of users that are used for taking the input are generally less than 100 which is not a good representation sample. The application of this technique is very cost effective as it does not require any extra hardware. Hence there is a need to share the datasets by researchers and develop a standard against which every researcher can compare his results. Also the environment in which the tests are performed should be uncontrolled which will give results that are more realistic and close to real deployment environment.

A Survey of behavioral authentication using keystroke dynamics: Touch screens and mobile devices

Nowadays most systems became computerized and use internet for remote access, including systems which have critical and sensitive data such as banks and governmental institutions. This led to the huge need for a reliable and efficient authentication system to secure data. User authentication is mostly done using passwords. But it is not a sufficient way to use just a password since it has many drawbacks, like guessing them, brute force attacks, key-loggers and social engineering. Additional authentication procedure is needed to enhance password security. Keystroke dynamics is one of the famous behavioral measurements that rely on utilizing the typing rhythm of each individual. It is used to strengthen password authentication in an efficient and cheap way since no hardware will be added. This paper presents a comprehensive survey on research in the last two decades on keystroke dynamics authentication. The objective is to discuss, summarize and provide insightful comparison about the well-known approaches used in keystroke dynamics such as statistical and neural network approaches, as well as offering suggestions and possible future research directions, especially for touch-screen and mobile devices. Keystroke dynamics could provide a second authentication factor for touch screen devices, as they are rapidly increasing in their use and are replacing the classical keyboards in the markets.

Authentication based on keystroke dynamics using stochastic diffusion algorithm

ABSTRACTAuthentication plays an important role in dealing with security. Securing sensitive data and computer systems by allowing easy access for authenticated users and withstanding the attacks of imposters is one of the major challenges in the field of computer security. Nowadays, passwords have become the trend to control access to computer systems. Biometrics are used to measure and analyze an individual's unique behavioral or physiological patterns for authentication purposes. Keystroke dynamics have emerged as an important method in analyzing the typing rhythm in biometric techniques, as they provide an ease of use and increased trustworthiness associated with biometrics for creating username and password schemes. In this experiment, we measure the Hausdorff timing values, mean, standard deviation, and median of keystroke features, such as latency, duration, digraph, and their combinations, and compare their performance. The stochastic diffusion search is used for feature subset selection.

Adaptive Positive Selection for Keystroke Dynamics

Current technologies provide state of the art services but, at the same time, increase data exposure, mainly due to Internet-based applications. In view of this scenario, improved authentication mechanisms are needed. Keystroke dynamics, which recognizes users by their typing rhythm, is a cost-effective alternative. This technology usually only requires a common keyboard in order to acquire authentication data. There are several studies investigating the use of machine learning techniques for user authentication based on keystroke dynamics. However, the majority of them assume a scenario which the user model is not updated. It is known that typing rhythm changes over time (concept drift). Consequently, classification algorithms in keystroke dynamics have to be able to adapt the user model to these changes. This paper evaluates adaptation methods for an immune positive selection algorithm in a data stream context. Experimental results showed that they improved classification performance, mainly for false rejection rates.

Study on Face Recognition Algorithm

Biometrics authentication is started to be used worldwide since the security it provides is of greater extends. Face recognition is an important method of such an authentication. Face recognition can be carried out by many algorithms such as PCA, LDA, ICA, BIC, AAM etc. In this paper, a detailed study about these algorithms is performed. The study deals with the definition part, methodology and the classification as well. Biometrics or biometric authentication is a way of identifying humans using their characteristics. It is a method of identification and access control. Biometric identifiers are categorized into physiological characteristics which are related to the shape of the body including fingerprints, face recognition ,iris recognition etc. and behavioral characteristics related to the pattern of behavior of person including typing rhythm, voice etc. Universality, uniqueness, measurability, permanence, performance, acceptability and circumvencce are the seven weighted factors for determining the biometrics for use in a particular application. Universality means that the person should posses the trait. Uniqueness means the trait should be unique. Permanence is the manner in which the trait varies over time. Measurability is the ease of acquisition. Acceptability is how others accept the technology. Performance refers to the accuracy and speed and robustness of the technology. Circumvention is the easiness in imitating the trait. A face recognition system compares the video or image of a person in database to the input captured. Comparison is done on the structure, shape and proportions of face. Distance between the eyes , jaw, mouth and nose. Several pictures of the subject at different angle with different facial expressions are enrolled to the database at the training phase. Identification and verification is performed by the subject standing in front of the camera for a few seconds. This image is compared to the one previously added. Different face recognition techniques are feature extraction, neural network, automatic face procession Eigen faces etc. Biometric techniques can be used for surveillance purpose. I.e. Face recognition can be done from a particular distance without having any contact with the subject so that they are unaware about it. Among several information in human faces, its efficient to choose the unique information while discarding other irrelevant information for making the system efficient. In this paper, a study on different face recognition algorithms is performed. These algorithms include PCA, LDA, BIC, ICA, AAM and Gabor Vectors.