A Keystroke-based Continuous User Authentication in Virtual Desktop Infrastructure

2021 ◽  
Author(s):  
Lulu Yang ◽  
Chen Li ◽  
Ruibang You ◽  
Bibo Tu
Keyword(s):  
User Authentication ◽  
Virtual Desktop ◽  
Continuous User ◽  
Virtual Desktop Infrastructure

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.

The Implementation of a GPU-Accelerated Virtual Desktop Infrastructure Platform

2017 ◽  
Author(s):  
Jheng-Yue Li ◽  
Chan-Fu Kuo ◽  
Yuan-Ting Wang ◽  
Ching-Fang Lee ◽  
Tzu-Yang Chen ◽  
...  
Keyword(s):  
Virtual Desktop ◽  
Virtual Desktop Infrastructure

Evidence in Hand: Passive Vibration Response-based Continuous User Authentication

2021 ◽  
Author(s):  
Hangcheng Cao ◽  
Hongbo Jiang ◽  
Daibo Liu ◽  
Jie Xiong
Keyword(s):  
User Authentication ◽  
Vibration Response ◽  
Continuous User

scholarly journals Analysis of a Secure Virtual Desktop Infrastructure System

2015 ◽  
Vol 7 (1) ◽  
pp. 69-84 ◽  
Author(s):  
Yi Jie Tong ◽  
Wei Qi Yan ◽  
Jin Yu
Keyword(s):  
Information Security ◽  
Data Storage ◽  
Storage Capacity ◽  
Cost Effective ◽  
Energy Costs ◽  
Campus Network ◽  
Effective Solution ◽  
Virtual Desktop ◽  
Infrastructure System ◽  
Virtual Desktop Infrastructure

With an increasing number of personal computers introduced in schools, enterprises and other large organizations, workloads of system administrators have been on the rise due to the issues related to energy costs, IT expenses, PC replacement expenditures, data storage capacity, and information security, etc. However, Application Virtualization (AV) has been proved as a successful cost-effective solution to solve these problems. In this paper, the analytics of a Virtual Desktop Infrastructure (VDI) system will be taken into consideration for a campus network. Our developed system will be introduced and justified. Furthermore, the rationality for these improvements will be introduced.

Continuous User Authentication Using Machine Learning on Touch Dynamics

2015 ◽  
pp. 591-598 ◽  
Author(s):  
Ştefania Budulan ◽  
Elena Burceanu ◽  
Traian Rebedea ◽  
Costin Chiru
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Continuous User

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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