scholarly journals S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3765
Author(s):  
Juan Manuel Espín López ◽  
Alberto Huertas Celdrán ◽  
Javier G. Marín-Blázquez ◽  
Francisco Esquembre ◽  
Gregorio Martínez Pérez
Keyword(s):  
Artificial Intelligence ◽  
Additional Contribution ◽  
User Profiles ◽  
Continuous Authentication ◽  
Promising Solution ◽  
Authentication System ◽  
Speaker Information ◽  
Current Systems

Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems. This work presents the S3 platform, an artificial intelligence-enabled continuous authentication system that combines data from sensors, applications statistics and voice to authenticate users in smartphones. Experiments have tested the relevance of each kind of data, explored different strategies to combine them, and determined how many days of training are needed to obtain good enough profiles. Results showed that voice is much more relevant than sensors and applications statistics when building a precise authenticating system, and the combination of individual models was the best strategy. Finally, the S3 platform reached a good performance with only five days of use available for training the users’ profiles. As an additional contribution, a dataset with 21 volunteers interacting freely with their smartphones for more than sixty days has been created and made available to the community.

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.

Multimodal Biometric Hand-Off for Robust Unobtrusive Continuous Biometric Authentication

2013 ◽  
pp. 389-409
Author(s):  
P. Daphne Tsatsoulis ◽  
Aaron Jaech ◽  
Robert Batie ◽  
Marios Savvides
Keyword(s):  
Tracking System ◽  
Operation Time ◽  
Face Verification ◽  
Computationally Efficient ◽  
Authorized User ◽  
Continuous Authentication ◽  
Training Stage ◽  
Hand Off ◽  
Authentication System ◽  
Computationally Intensive

Conventional access control solutions rely on a single authentication to verify a user’s identity but do nothing to ensure the authenticated user is indeed the same person using the system afterwards. Without continuous monitoring, unauthorized individuals have an opportunity to “hijack” or “tailgate” the original user’s session. Continuous authentication attempts to remedy this security loophole. Biometrics is an attractive solution for continuous authentication as it is unobtrusive yet still highly accurate. This allows the authorized user to continue about his routine but quickly detects and blocks intruders. This chapter outlines the components of a multi-biometric based continuous authentication system. Our application employs a biometric hand-off strategy where in the first authentication step a strong biometric robustly identifies the user and then hands control to a less computationally intensive face recognition and tracking system that continuously monitors the presence of the user. Using multiple biometrics allows the system to benefit from the strengths of each modality. Since face verification accuracy degrades as more time elapses between the training stage and operation time, our proposed hand-off strategy permits continuous robust face verification with relatively simple and computationally efficient classifiers. We provide a detailed evaluation of verification performance using different pattern classification algorithms and show that the final multi-modal biometric hand-off scheme yields high verification performance.

Continuous Authentication in Computers

2013 ◽  
pp. 268-293
Author(s):  
Harini Jagadeesan ◽  
Michael S. Hsiao
Keyword(s):  
User Behavior ◽  
Behavioral Pattern ◽  
Security Issue ◽  
Age Identity ◽  
Internet Age ◽  
Continuous Authentication ◽  
System Logs ◽  
Computer Based ◽  
Authentication System ◽  
High Degree

In the Internet age, identity theft is a major security issue because contemporary authentication systems lack adequate mechanisms to detect and prevent masquerading. This chapter discusses the current authentication systems and identifies their limitations in combating masquerading attacks. Analysis of existing authentication systems reveals the factors to be considered and the steps necessary in building a good continuous authentication system. As an example, we present a continual, non-intrusive, fast and easily deployable user re-authentication system based on behavioral biometrics. It employs a novel heuristic based on keyboard and mouse attributes to decipher the behavioral pattern of each individual user on the system. In the re-authentication process, the current behavior of user is compared with stored “expected” behavior. If user behavior deviates from expected behavior beyond an allowed threshold, system logs the user out of the current session, thereby preventing imposters from misusing the system. Experimental results show that the proposed methodology improves the accuracy of application-based and application independent systems to 96.4% and 82.2% respectively. At the end of this chapter, the reader is expected to understand the dimensions involved in creating a computer based continuous authentication system and is able to frame a robust continual re-authentication system with a high degree of accuracy.

scholarly journals Design, implementation, and assessment of a usable multi-biometric continuous authentication system

2019 ◽  
Vol 9 (3) ◽  
pp. 215
Author(s):  
Enrico Schiavone ◽  
Andrea Ceccarelli ◽  
Ariadne Carvalho ◽  
Andrea Bondavalli
Keyword(s):  
Continuous Authentication ◽  
Authentication System

scholarly journals Mouse Behavior Analysis Based on Artificial Intelligence as a Second-Phase Authentication System

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 29
Author(s):  
Daniel Garabato ◽  
Jorge Rodríguez García ◽  
Francisco J. Novoa ◽  
Carlos Dafonte
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machines ◽  
Support Vector ◽  
Second Phase ◽  
Authentication Protocols ◽  
Mouse Behavior ◽  
Authentication Mechanism ◽  
Vector Machines ◽  
Authentication System ◽  
Current Stage

Nowadays, a wide variety of computer systems use authentication protocols based on several factors in order to enhance security. In this work, the viability of a second-phase authentication scheme based on users’ mouse behavior is analyzed by means of classical Artificial Intelligence techniques, such as the Support Vector Machines or Multi-Layer Perceptrons. Such methods were found to perform particularly well, demonstrating the feasibility of mouse behavior analytics as a second-phase authentication mechanism. In addition, in the current stage of the experiments, the classification techniques were found to be very stable for the extracted features.

scholarly journals A Pattern-Based Multi-Factor Authentication System

2019 ◽  
Vol 20 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Pankhuri . ◽  
Akash Sinha ◽  
Gulshan Shrivastava ◽  
Prabhat Kumar
Keyword(s):  
Artificial Intelligence ◽  
User Authentication ◽  
Authentication Scheme ◽  
Brute Force ◽  
Security Attacks ◽  
Graphical Password ◽  
Efficient Strategy ◽  
Shoulder Surfing ◽  
Different Types ◽  
Authentication System

User authentication is an indispensable part of a secure system. The traditional authentication methods have been proved to be vulnerable to different types of security attacks. Artificial intelligence is being applied to crack textual passwords and even CAPTCHAs are being dismantled within few attempts. The use of graphical password as an alternate to the textual passwords for user authentication can be an efficient strategy. However, they have been proved to be susceptible to shoulder surfing like attacks. Advanced authentication systems such as biometrics are secure but require additional infrastructure for efficient implementation. This paper proposes a novel pattern-based multi-factor authentication scheme that uses a combination of text and images resulting for identifying the legitimate users. The proposed system has been mathematically analyzed and has been found to provide much larger password space as compared to simple text based passwords. This renders the proposed system secure against brute force and other dictionary based attacks. Moreover, the use of text along with the images also mitigates the risk of shoulder surfing.

Continuous Authentication in Computers

2011 ◽  
pp. 40-66 ◽  
Author(s):  
Harini Jagadeesan ◽  
Michael S. Hsiao
Keyword(s):  
User Behavior ◽  
Behavioral Pattern ◽  
Security Issue ◽  
Age Identity ◽  
Internet Age ◽  
Continuous Authentication ◽  
System Logs ◽  
Computer Based ◽  
Authentication System ◽  
High Degree

In the Internet age, identity theft is a major security issue because contemporary authentication systems lack adequate mechanisms to detect and prevent masquerading. This chapter discusses the current authentication systems and identifies their limitations in combating masquerading attacks. Analysis of existing authentication systems reveals the factors to be considered and the steps necessary in building a good continuous authentication system. As an example, we present a continual, non-intrusive, fast and easily deployable user re-authentication system based on behavioral biometrics. It employs a novel heuristic based on keyboard and mouse attributes to decipher the behavioral pattern of each individual user on the system. In the re-authentication process, the current behavior of user is compared with stored “expected” behavior. If user behavior deviates from expected behavior beyond an allowed threshold, system logs the user out of the current session, thereby preventing imposters from misusing the system. Experimental results show that the proposed methodology improves the accuracy of application-based and application independent systems to 96.4% and 82.2% respectively. At the end of this chapter, the reader is expected to understand the dimensions involved in creating a computer based continuous authentication system and is able to frame a robust continual re-authentication system with a high degree of accuracy.

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