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.

scholarly journals Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3769 ◽  
Author(s):  
José Jorquera Valero ◽  
Pedro Sánchez Sánchez ◽  
Lorenzo Fernández Maimó ◽  
Alberto Huertas Celdrán ◽  
Marcos Arjona Fernández ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Quality Of Experience ◽  
Machine Learning Techniques ◽  
Proof Of Concept ◽  
Design And Implementation ◽  
Continuous Authentication ◽  
Learning Techniques ◽  
Authentication System ◽  
Critical Aspects

Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users’ quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users’ behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users’ authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level.

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.

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.

Authentication System for Biometric Applications Using Mobile Devices

2013 ◽  
Vol 457-458 ◽  
pp. 1224-1227
Author(s):  
Jian Feng Hu ◽  
Zhen Dong Mu
Keyword(s):  
Information Security ◽  
Mobile Devices ◽  
Information Exchange ◽  
Mobile Platform ◽  
Eeg Signal ◽  
Information Protection ◽  
Security Research ◽  
Authentication System ◽  
Identification Tool

Mobile equipment has now become a new platform for information exchange, spend a lot of information exchange, how to effectively protect the mobile platform information security? Research has shown that, EEG signal can be used as identification tool, the user's information protection and good, this paper to protect the information security of mobile devices to research how to use EEG; the EEG signal is feasible for mobile equipment identification.

scholarly journals Usability of an Affective Emotional Learning Tutoring System for Mobile Devices

2021 ◽  
Vol 13 (14) ◽  
pp. 7890
Author(s):  
Tao-Hua Wang ◽  
Hao-Chiang Koong Lin ◽  
Hong-Ren Chen ◽  
Yueh-Min Huang ◽  
Wei-Ting Yeh ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Mobile Devices ◽  
User Satisfaction ◽  
Quantitative Research ◽  
Negative Emotions ◽  
User Interaction ◽  
Feedback Mechanism ◽  
Quality Education ◽  
Tutoring System ◽  
System Usability Scale

To echo the United Nations formulated Sustainable Development Goals (SDGs), SDG 4 is to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Furthermore, high-quality education is the base on which human lives can be improved and sustainable development can be accomplished. Therefore, the affective emotional tutoring system established in this study enables learning via mobile devices, which are indispensable in daily life. The real-time interactive agent in the system guides learners to turn negative emotions into positive ones. We explored the usability of and user satisfaction with the affective emotional tutoring system. Sixty-two students participated in the study which used a quantitative research design to explore a learning situation. The overall usability of the system was evaluated with the System Usability Scale (SUS), and the Questionnaire for User Interaction Satisfaction (QUIS) was used to evaluate user satisfaction with the different elements of the system. The results showed that both the usability of and satisfaction with the affective emotional tutoring system were high. The emotional feedback mechanism of the system can help learners turn negative emotions into positive ones.

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.

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