Continuous User Authentication System: A Risk Analysis Based Approach

Smart health (s-health) is a vital topic and an essential research field today, supporting the real-time monitoring of user’s data by using sensors, either in direct or indirect contact with the human body. Real-time monitoring promotes changes in healthcare from a reactive to a proactive paradigm, contributing to early detection, prevention, and long-term management of health conditions. Under these new conditions, continuous user authentication plays a key role in protecting data and access control, once it focuses on keeping track of a user’s identity throughout the system operation. Traditional user authentication systems cannot fulfill the security requirements of s-health, because they are limited, prone to security breaches, and require the user to frequently authenticate by, e.g., a password or fingerprint. This interrupts the normal use of the system, being highly inconvenient and not user friendly. Also, data transmission in current authentication systems relies on wireless technologies, which are susceptible to eavesdropping during the pairing stage. Biological signals, e.g., electrocardiogram (ECG) and electroencephalogram (EEG), can offer continuous and seamless authentication bolstered by exclusive characteristics from each individual. However, it is necessary to redesign current authentication systems to encompass biometric traits and new communication technologies that can jointly protect data and provide continuous authentication. Hence, this article presents a novel biosignal authentication system, in which the photoplethysmogram (PPG) biosignal and a galvanic coupling (GC) channel lead to continuous, seamless, and secure user authentication. Furthermore, this article contributes to a clear organization of the state of the art on biosignal-based continuous user authentication systems, assisting research studies in this field. The evaluation of the system feasibility presents accuracy in keeping data integrity and up to 98.66% accuracy in the authentication process.

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Mobile-based continuous user authentication system for cloud security

2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) ◽

10.1109/icnidc.2016.7974559 ◽

2016 ◽

Cited By ~ 2

Author(s):

Qing Li ◽

Le Wang ◽

TaeGuen Kim ◽

Eul Gyu Im

Keyword(s):

User Authentication ◽

Cloud Security ◽

Continuous User ◽

Authentication System

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On keystrokes as Continuous User Biometric Authentication

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9301.088619 ◽

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.

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Concealable Biometric-based Continuous User Authentication System An EEG Induced Deep Learning Model

2021 IEEE International Joint Conference on Biometrics (IJCB) ◽

10.1109/ijcb52358.2021.9484345 ◽

2021 ◽

Author(s):

Sindhu Reddy Kalathur Gopal ◽

Diksha Shukla

Keyword(s):

Deep Learning ◽

User Authentication ◽

Learning Model ◽

Continuous User ◽

Authentication System ◽

Deep Learning Model

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

Sensors ◽

10.3390/s21124212 ◽

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.

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