76 research outputs found
Performance Evaluation of Biometric Template Update
Template update allows to modify the biometric reference of a user while he
uses the biometric system. With such kind of mechanism we expect the biometric
system uses always an up to date representation of the user, by capturing his
intra-class (temporary or permanent) variability. Although several studies
exist in the literature, there is no commonly adopted evaluation scheme. This
does not ease the comparison of the different systems of the literature. In
this paper, we show that using different evaluation procedures can lead in
different, and contradictory, interpretations of the results. We use a
keystroke dynamics (which is a modality suffering of template ageing quickly)
template update system on a dataset consisting of height different sessions to
illustrate this point. Even if we do not answer to this problematic, it shows
that it is necessary to normalize the template update evaluation procedures.Comment: International Biometric Performance Testing Conference 2012,
Gaithersburg, MD, USA : United States (2012
Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis
Most keystroke dynamics studies have been evaluated using a specific kind of
dataset in which users type an imposed login and password. Moreover, these
studies are optimistics since most of them use different acquisition protocols,
private datasets, controlled environment, etc. In order to enhance the accuracy
of keystroke dynamics' performance, the main contribution of this paper is
twofold. First, we provide a new kind of dataset in which users have typed both
an imposed and a chosen pairs of logins and passwords. In addition, the
keystroke dynamics samples are collected in a web-based uncontrolled
environment (OS, keyboards, browser, etc.). Such kind of dataset is important
since it provides us more realistic results of keystroke dynamics' performance
in comparison to the literature (controlled environment, etc.). Second, we
present a statistical analysis of well known assertions such as the
relationship between performance and password size, impact of fusion schemes on
system overall performance, and others such as the relationship between
performance and entropy. We put into obviousness in this paper some new results
on keystroke dynamics in realistic conditions.Comment: The Eighth International Conference on Intelligent Information Hiding
and Multimedia Signal Processing (IIHMSP 2012), Piraeus : Greece (2012
Hybrid Template Update System for Unimodal Biometric Systems
Semi-supervised template update systems allow to automatically take into
account the intra-class variability of the biometric data over time. Such
systems can be inefficient by including too many impostor's samples or skipping
too many genuine's samples. In the first case, the biometric reference drifts
from the real biometric data and attracts more often impostors. In the second
case, the biometric reference does not evolve quickly enough and also
progressively drifts from the real biometric data. We propose a hybrid system
using several biometric sub-references in order to increase per- formance of
self-update systems by reducing the previously cited errors. The proposition is
validated for a keystroke- dynamics authentication system (this modality
suffers of high variability over time) on two consequent datasets from the
state of the art.Comment: IEEE International Conference on Biometrics: Theory, Applications and
Systems (BTAS 2012), Washington, District of Columbia, USA : France (2012
Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication
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
KeyDetect --Detection of anomalies and user based on Keystroke Dynamics
Cyber attacks has always been of a great concern. Websites and services with
poor security layers are the most vulnerable to such cyber attacks. The
attackers can easily access sensitive data like credit card details and social
security number from such vulnerable services. Currently to stop cyber attacks,
various different methods are opted from using two-step verification methods
like One-Time Password and push notification services to using high-end
bio-metric devices like finger print reader and iris scanner are used as
security layers. These current security measures carry a lot of cons and the
worst is that user always need to carry the authentication device on them to
access their data. To overcome this, we are proposing a technique of using
keystroke dynamics (typing pattern) of a user to authenticate the genuine user.
In the method, we are taking a data set of 51 users typing a password in 8
sessions done on alternate days to record mood fluctuations of the user.
Developed and implemented anomaly-detection algorithm based on distance metrics
and machine learning algorithms like Artificial Neural networks (ANN) and
convolutional neural network (CNN) to classify the users. In ANN, we
implemented multi-class classification using 1-D convolution as the data was
correlated and multi-class classification with negative class which was used to
classify anomaly based on all users put together. We were able to achieve an
accuracy of 95.05% using ANN with Negative Class. From the results achieved, we
can say that the model works perfectly and can be bought into the market as a
security layer and a good alternative to two-step verification using external
devices. This technique will enable users to have two-step security layer
without worrying about carry an authentication device
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
One-handed keystroke biometric identification competition
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. V. Monaco, G. Perez, C. C. Tappert, P. Bours, S. Modal, S. Rajkumar, A. Morales, J. Fierrez, and J. Ortega-Garcia, "One-handed Keystroke Biometric Identification Competition", in International Conference on Biometrics, ICB 2015, 58-64This work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.The authors would like to acknowledge the support from
the National Science Foundation under Grant No. 1241585.
Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the authors and
do not necessarily reflect the views of the National Science
Foundation or the US government
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