On Continuous User Authentication via Hidden Free-Text Based Monitoring

2018 ◽  
pp. 66-75
Author(s):  
Elena Kochegurova ◽  
Elena Luneva ◽  
Ekaterina Gorokhova
Keyword(s):  
User Authentication ◽  
Free Text ◽  
Continuous User

scholarly journals On keystrokes as Continuous User Biometric Authentication

2019 ◽  
Vol 8 (6) ◽  
pp. 4149-4153
Keyword(s):  
User Authentication ◽  
Support Vector ◽  
Free Text ◽  
Biometric Authentication ◽  
Text Input ◽  
Continuous Authentication ◽  
Online Examination ◽  
Continuous User ◽  
Authentication System ◽  
Identification Phase

Authentication of a user through an ID and password is generally done at the start of a session. But the continuous authentication system observe the genuineness of the user throughout the entire session, and not at login only. In this paper, we propose the usage of keystroke dynamics as biometric trait for continuous user authentication in desktop platform. Biometric Authentication involves mainly three phases named as enrollment phase, verification phase and identification phase. The identification phase marks the accessed user as an authenticated only if the input pattern matches with the profile pattern otherwise the system is logout. The proposed Continuous User Biometric Authentication (CUBA) System is based on free text input from keyboard. There is no restriction on input data during Enrolment, Verification, and Identification phase. Unsupervised One-class Support Vector Machine is used to classify the authenticated user’s input from all the other inputs. This continuous authentication system can be used in many areas like in Un-proctored online examination systems, Intrusion & Fraud Detection Systems, Areas where user alertness is required for entire period e.g. Controlling Air Traffic etc.

scholarly journals A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4212
Author(s):  
Priscila Morais Argôlo Bonfim Estrela ◽  
Robson de Oliveira Albuquerque ◽  
Dino Macedo Amaral ◽  
William Ferreira Giozza ◽  
Rafael Timóteo de Sousa Júnior
Keyword(s):  
User Authentication ◽  
Internet Banking ◽  
Supervised Machine Learning ◽  
Smart Devices ◽  
Static Verification ◽  
User Behaviors ◽  
Legitimate User ◽  
Continuous User ◽  
Mobile Banking Applications ◽  
Effective Layer

As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication.

An ear canal deformation based continuous user authentication using earables

2021 ◽  
Author(s):  
Zi Wang ◽  
Sheng Tan ◽  
Linghan Zhang ◽  
Yili Ren ◽  
Zhi Wang ◽  
...  
Keyword(s):  
User Authentication ◽  
Ear Canal ◽  
Continuous User

Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

2011 ◽  
pp. 232-252
Author(s):  
Sérgio Roberto de Lima e Silva Filho ◽  
Mauro Roisenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
User Authentication ◽  
Keystroke Dynamics ◽  
Continuous Authentication ◽  
Biometric Characteristic ◽  
Continuous User ◽  
Computer Keyboard

This chapter proposes an authentication methodology that is both inexpensive and non-intrusive and authenticates users continuously while using a computer keyboard. This proposed methodology uses neural network committee machines. The committee consists of several independent neural networks trained to recognize a behavioral biometric characteristic: user’s typing pattern. Continuous authentication prevents potential attacks when users leave their desks without logging out or locking their computer session. Some experiments were conducted to evaluate and to calibrate the authentication committee. Best results show that a 0% FAR and a 0.15% FRR can be achieved when different thresholds are used in the system for each user. In this proposed methodology, capture system does not need to concern about typing errors in the text. Another feature of this methodology is that new users can be easily added to the system, with no need to re-train all neural networks involved.

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