Keystroke Dynamics Based User Authentication using Numeric Keypad (original) (raw)
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KSII Transactions on Internet and Information Systems
Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.
User Authentication Using Keystroke Dynamics based of Selected Features
Keystroke dynamics is the time analysis of a users typing habit. Keystroke dynamics is a biometric method of user authentication. However an authentication technique must strive to balance two properties: speed; accuracy. In order to balance these conflicting properties, a small set of accurate features and a simplistic measurement technique. These are the measures of flight time, dwell time, and latency time, and the technique of confidence interval based statistical analysis.
A Study And Analysis of Keystroke Dynamics And Its Enhancement For Proficient User Authentication
in this paper we proposed one new measure of keystroke patterns over and above of the existing features for making user authentication through keystroke more efficient. With comparison to other access control systems based on biometric features, keystroke analysis has not yet meets acceptable level of accuracy. The reason is probably the intrinsic variability of typing dynamics, versus other very stable biometric characteristics, such as face or fingerprint. Our experiment and statistical analysis described in the current literature and show through experimental data that, the proposed unique measure of keystrokes can be combined with existing authentication mechanism to improve the authentication and security of delicate applications to a very high extent. It can be useful to ascertain the intruders and reject them from the system, provided that we are able to deal with the typing rhythm of the intruders. Our methodology can rely on what is typed by people because of their normal j...
Keystroke Dynamics-Based Authentication Using Unique Keypad
Sensors, 2021
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 th...
Fixed-Text vs. Free-Text Keystroke Dynamics for User Authentication
Engineering Research Journal - Faculty of Engineering (Shoubra), 2022
There are many physical biometrics such as iris patterns and fingerprints. There are also interactive gestures like how a person types on a keyboard, moves a mouse, holds a phone, or even taps a touch screen. Keystroke dynamics or typing dynamics is an automatic method that confirms the identity of an individual based on the manner and the way of the user typing on a keyboard. There are two types of keystroke systems, Fixed-text system, and free-text system and each of them has it is own importance. In this research paper, we are investigating the possibility of classifying individuals using features extracted from their keystroke dynamics with two different datasets: (1) fixed-text dataset with different difficulty levels and (2) free-text dataset with no restrictions what a user types on the keyboard. Investigation was done using several classification techniques: RandomForest (RF), Support Vector Machines (SVM), BayesNet (BN), and K-Nearest Neighbors (KNN). The highest accuracy achieved with the fixed-text dataset was 98.8% using RF for classification while the highest achieved accuracy with the freetext dataset was 87.58 % using RF classifier.
User Authentication using Keystroke Dynamics
There is need to secure sensitive data and computer systems from intruders while allowing ease of access for authenticating the user is one of the main problems in computer security. Traditionally, passwords have been the usual method for controlling access to computer systems but this approach has many inherent flaws. Keystroke dynamics is a biometric technique to recognize and an analysis of his/her typing patterns. In the experiment, we measure mean, standard deviation and median values of keystroke features such as latency, duration, digraph and their combinations and compare their performance. The latest trend in authenticating users is by using the potentiality of biometrics. Keystroke dynamics is a behavioral biometrics which captures the typing rhythms of users and then authenticates them based on the dynamics captured. In this paper, a detailed study on the evaluation of keystroke dynamics as a measure of authentication is carried out. This paper gives an insight from the infancy stage to the current work done on this domain which can be used by researchers working on this topic.
A machine learning approach to keystroke dynamics based user authentication
International Journal of …, 2007
The majority of computer systems employ a login ID and password as the principal method for access security. In stand-alone situations, this level of security may be adequate, but when computers are connected to the internet, the vulnerability to a security breach is increased. In order to reduce vulnerability to attack, biometric solutions have been employed. In this paper, we investigate the use of a behavioural biometric based on keystroke dynamics. Although there are several implementations of keystroke dynamics available -their effectiveness is variable and dependent on the data sample and its acquisition methodology. The results from this study indicate that the Equal Error Rate (EER) is significantly influenced by the attribute selection process and to a lesser extent on the authentication algorithm employed. Our results also provide evidence that a Probabilistic Neural Network (PNN) can be superior in terms of reduced training time and classification accuracy when compared with a typical MLFN back-propagation trained neural network.
User Authentication by Keystroke Dynamics Using Machine Learning Algorithms
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019
Due to the expanding vulnerabilities in cyber forensics, security alone is not sufficient to forestall a rupture; however, cyber security is additionally required to anticipate future assaults or to distinguish the potential aggressor. Keystroke Dynamics has high use in cyber intelligence. The paper examines the helpfulness of keystroke dynamics to build up the individual personality. Three schemes are proposed for recognizing an individual while typing on keyboard. Lib SVM and binary SVM are proposed and their performance are shown. Lib SVM is showing a better performance when comparing with binary SVM. As the number of samples are increased it shows an increase in the accuracy. Pair wise user coupling technique is proposed. The proposed procedures are approved by utilizing keystroke information. In any case, these systems could similarly well be connected to other examples of pattern identification problems. This system is applicable in highly confidential areas like military.
Keystroke Dynamics Based User Authentication Using Neural Networks
Artificial Neural Nets and Genetic Algorithms, 1995
User authentication is a crucial requirement for cloud service providers to prove that the outsourced data and services are safe from imposters. Keystroke dynamics is a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions designed for certain datasets, for example, a fixed length text typed on a traditional personal computer keyboard and their authentication performances were not acceptable for other input devices nor free length text. Moreover, they suffer from a high dimensional feature space that degrades the authentication accuracy and performance. In this paper, a keystroke dynamics based authentication system is proposed for cloud environments that is applicable to fixed and free text typed on traditional and touch screen keyboards. The proposed system utilizes different feature extraction methods, as a preprocessing step, to minimize the feature space dimensionality. Moreover, different fusion rules are evaluated to combine the different feature extraction methods so that a set of the most relevant features is chosen. Because of the huge number of users' samples, a clustering method is applied to the users' profile templates to reduce the verification time. The proposed system is applied to three different benchmark datasets using three different classifiers. Experimental results demonstrate the effectiveness and efficiency of the proposed system.
Keystroke Dynamics for User Authentication and Identification by using Typing Rhythm
International Journal of Computer Applications, 2016
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.