scholarly journals Multimodal Continuous User Authentication on Mobile Devices via Interaction Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaomei Zhang ◽  
Pengming Zhang ◽  
Haomin Hu
Keyword(s):  
Mobile Devices ◽  
User Authentication ◽  
User Interaction ◽  
Behavior Modeling ◽  
Interaction Patterns ◽  
Hand Motion ◽  
Static Interaction ◽  
Continuous Authentication ◽  
Multimodal Systems ◽  
Continuous User

Behavior-based continuous authentication is an increasingly popular methodology that utilizes behavior modeling and sensing for authentication and account access authorization. As an appearing behavioral biometric, user interaction patterns with mobile devices focus on verifying their identity in terms of their features or operating styles while interacting with devices. However, unimodal continuous authentication schemes, which are on the basis of a single source of interaction information, can only deal with a particular action or scenario. Hence, multimodal systems should be taken to suit for various environmental conditions especially in circumstances of attacks. In this paper, we propose a multimodal continuous authentication method both based on static interaction patterns and dynamic interaction patterns with mobile devices. Behavioral biometric features, HMHP, which is combined hand motion (HM) and hold posture (HP), are essentially established upon the touch screen and accelerator and capture the variation model of microhand motions and hold patterns generated in both dynamic and static scenes. By combining the features of HM and HP, the fusion feature HMHP achieves 97% accuracy with a 3.49% equal error rate.

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.

Intelligent User Profiling Based on Sensors and Location Data to Detect Intrusions on Mobile Devices

2020 ◽  
pp. 1-25
Author(s):  
Pedro Miguel Sánchez Sánchez ◽  
José María Jorquera Valero ◽  
Alberto Huertas Celdran ◽  
Gregorio Martínez Pérez
Keyword(s):  
Mobile Devices ◽  
User Interaction ◽  
Machine Learning Algorithms ◽  
User Profiling ◽  
Normal Behaviour ◽  
Location Data ◽  
Secure Access ◽  
Continuous Authentication ◽  
Promising Solution ◽  
Authentication System

Continuous authentication systems are considered as a promising solution to secure access to mobile devices. Their main benefit is the improvement of the users' experience when they use the services or applications of their mobile device. Specifically, continuous authentication avoids having to remember or possess any key to access an application or service that requires authentication. In this sense, having the user authenticated permanently increases the security of the device. It also allows the user interaction with applications to be much more fluid, simple, and satisfactory. This chapter proposes a new continuous authentication system for mobile devices. The system acquires data from the device sensors and the GPS location to create a dataset that represents the user's profile or normal behaviour. Then, the proposed system uses Machine Learning algorithms based on anomaly detection to perform user identification in real time. Several experiments have been carried out to demonstrate the performance and usefulness of the proposed solution.

A Lightweight Privacy-Aware Continuous Authentication Protocol-PACA

2021 ◽  
Vol 24 (4) ◽  
pp. 1-28
Author(s):  
Abbas Acar ◽  
Shoukat Ali ◽  
Koray Karabina ◽  
Cengiz Kaygusuz ◽  
Hidayet Aksu ◽  
...  
Keyword(s):  
Template Matching ◽  
User Authentication ◽  
Third Party ◽  
Biometric Data ◽  
Concrete System ◽  
Current State ◽  
Continuous Authentication ◽  
User Data ◽  
Continuous User ◽  
Privacy Issues

As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, <underline>p</underline>rivacy-<underline>a</underline>ware, and secure <underline>c</underline>ontinuous <underline>a</underline>uthentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously authenticates users based on their biometrics in a privacy-aware manner. Then, we design an actual continuous user authentication system under the proposed protocol. In this concrete system, we utilize a privacy-aware template matching technique and a wearable-assisted keystroke dynamics-based continuous authentication method. This provides privacy guarantees without relying on any trusted third party while allowing the comparison of noisy user inputs (due to biometric data) and yielding an efficient and lightweight protocol. Finally, we implement our system on an Apple smartwatch and perform experiments with real user data to evaluate the accuracy and resource consumption of our concrete system.

Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges

2016 ◽  
Vol 33 (4) ◽  
pp. 49-61 ◽  
Author(s):  
Vishal M. Patel ◽  
Rama Chellappa ◽  
Deepak Chandra ◽  
Brandon Barbello
Keyword(s):  
Mobile Devices ◽  
Recent Progress ◽  
User Authentication ◽  
Continuous User

scholarly journals Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4592
Author(s):  
Xin Zeng ◽  
Xiaomei Zhang ◽  
Shuqun Yang ◽  
Zhicai Shi ◽  
Chihung Chi
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
User Authentication ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Edge Computing ◽  
Behavior Modeling ◽  
Security And Privacy ◽  
Positive Rate ◽  
Edge Based

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

scholarly journals User Authentication based on Continuous Touch Biometrics

2016 ◽  
Vol 28 (2) ◽  
Author(s):  
Christina J Kroeze ◽  
Katherine Mary Malan
Keyword(s):  
Mobile Devices ◽  
Real World ◽  
User Authentication ◽  
Classification Algorithms ◽  
Biometric Data ◽  
Continuous Authentication ◽  
Touch Interaction ◽  
Finger Pressure

Mobile devices such as smartphones have until now been protected by traditional authentication methods, including passwords or pattern locks. These authentication mechanisms are difficult to remember and are often disabled, leaving the device vulnerable if stolen. This paper investigates the possibility of unobtrusive, continuous authentication for smartphones based on biometric data collected using a touchscreen. The possibility of authenticating users on a smartphone was evaluated by conducting an experiment simulating real-world touch interaction. Touch data was collected from 30 participants during normal phone use. The touch features were analysed in terms of the information provided for authentication. It was found that features such as finger pressure, location of touch interaction and shape of the finger were important discriminators for authentication. The touch data was also analysed using two classification algorithms to measure the authentication accuracy. The results show that touch data is sufficiently distinct between users to be used in authentication without disrupting normal touch interaction. It is also shown that the raw touch data was more effective in authentication than the aggregated gesture data.

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

2013 ◽  
pp. 410-429
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.

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.

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