scholarly journals Keystroke Dynamics-Based Authentication Using Unique Keypad

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2242
Author(s):  
Maro Choi ◽  
Shincheol Lee ◽  
Minjae Jo ◽  
Ji Sun Shin
Keyword(s):  
User Authentication ◽  
Classification Performance ◽  
Performance Outcomes ◽  
Error Rates ◽  
Personal Identification ◽  
Biometric Authentication ◽  
Keystroke Dynamics ◽  
User Classification ◽  
Wide Range ◽  
Filtering Rates

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.

scholarly journals Age Detection Through Keystroke Dynamics from User Authentication Failures

2017 ◽  
Vol 9 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Ioannis Tsimperidis ◽  
Shahin Rostami ◽  
Vasilios Katos
Keyword(s):  
Random Distribution ◽  
User Authentication ◽  
Biometric Authentication ◽  
Incident Response ◽  
Multi Layer Perceptron ◽  
Keystroke Dynamics ◽  
The Subject ◽  
Main Component ◽  
User Recognition

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.

Behavioral Biometrics

2010 ◽  
pp. 101-120 ◽  
Author(s):  
Kenneth Revett
Keyword(s):  
User Authentication ◽  
Error Rates ◽  
Keystroke Dynamics ◽  
Behavioral Biometrics ◽  
Authentication Mechanism ◽  
Computer Based ◽  
Authentication System ◽  
Novel Approaches ◽  
New Form ◽  
Minimal Effort

Behavioral biometrics is a relatively new form of authentication mechanism which relies on the way a person interacts with an authentication device. Traditional instances of this approach include voice, signature, and keystroke dynamics. Novel approaches to behavioral biometrics include biosignals, such as the electroencephalogram and the electrocardiogram. The biosignal approach to user authentication has been shown to produce equal error rates on par with more traditional behavioral biometric approaches. In addition, through a process similar to biofeedback, users can be trained with minimal effort to produce computer-based input via the manipulations of endogenous biosignal patterns. This chapter discusses the use of biosignal based biometrics, highlighting key studies and how this approach can be integrated into a multibiometric user authentication system.

scholarly journals On the Design and Analysis of a Biometric Authentication System Using Keystroke Dynamics

Cryptography ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 12
Author(s):  
Robert Cockell ◽  
Basel Halak
Keyword(s):  
User Authentication ◽  
Training Data ◽  
Average Error ◽  
Biometric Authentication ◽  
Keystroke Dynamics ◽  
Systematic Analysis ◽  
Average Error Rate ◽  
Multifactor Authentication ◽  
Authentication System ◽  
Cost Efficient

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%.

scholarly journals Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 835
Author(s):  
Ioannis Tsimperidis ◽  
Cagatay Yucel ◽  
Vasilios Katos
Keyword(s):  
High Accuracy ◽  
Keystroke Dynamics ◽  
Time Intervals ◽  
Age And Gender ◽  
Common Features ◽  
User Classification ◽  
Gender And Age ◽  
And Gender ◽  
Keystroke Dynamic

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.

scholarly journals A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 234 ◽  
Author(s):  
Sumet Mehta ◽  
Xiangjun Shen ◽  
Jiangping Gou ◽  
Dejiao Niu
Keyword(s):  
Small Sample Size ◽  
Classification Performance ◽  
Error Rates ◽  
Small Sample ◽  
Harmonic Mean ◽  
Local Means ◽  
Local Mean ◽  
Real World Datasets ◽  
Nearest Neighbour Classifier ◽  
Query Sample

The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each class are found which are used to find k different local mean vectors, and then employed to compute their harmonic mean distance to the query sample. Lastly, the query sample is assigned to the class with minimum harmonic mean distance. The experimental results based on twenty-six real-world datasets shows that the proposed LMKHNCN classifier achieves lower error rates, particularly in small sample-size situations, and that it is less sensitive to parameter k when compared to therelated four KNN-based classifiers.

scholarly journals Markovian analysis of cervical cell images.

1976 ◽  
Vol 24 (1) ◽  
pp. 138-144 ◽  
Author(s):  
N J Pressman
Keyword(s):  
Transition Probabilities ◽  
Classification Performance ◽  
Error Rates ◽  
Classification Error ◽  
Correct Classification ◽  
Cell Nuclei ◽  
Step Size ◽  
Cervical Cell ◽  
Texture Parameters ◽  
Markovian Analysis

Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. Experiments are described that investigate that classification performance of parameters generated by Markovian analysis. Results using Markov texture parameters show that the selection of a Markov step size strongly affects classification error rates and the number of parameters required to achieve the maximum correct classification rates. Markov texture parameters are shown to achieve high rates of correct classification in discriminating images of normal from abnormal cervical cell nuclei.

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