1,358 research outputs found
Secure Pick Up: Implicit Authentication When You Start Using the Smartphone
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device,
non-intrusive and automatic-learning system for smartphone user authentication.
Operating in the background, our system implicitly observes users' phone
pick-up movements, the way they bend their arms when they pick up a smartphone
to interact with the device, to authenticate the users.
Our SPU outperforms the state-of-the-art implicit authentication mechanisms
in three main aspects: 1) SPU automatically learns the user's behavioral
pattern without requiring a large amount of training data (especially those of
other users) as previous methods did, making it more deployable. Towards this
end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW)
algorithm to effectively quantify similarities between users' pick-up
movements; 2) SPU does not rely on a remote server for providing further
computational power, making SPU efficient and usable even without network
access; and 3) our system can adaptively update a user's authentication model
to accommodate user's behavioral drift over time with negligible overhead.
Through extensive experiments on real world datasets, we demonstrate that SPU
can achieve authentication accuracy up to 96.3% with a very low latency of 2.4
milliseconds. It reduces the number of times a user has to do explicit
authentication by 32.9%, while effectively defending against various attacks.Comment: Published on ACM Symposium on Access Control Models and Technologies
(SACMAT) 201
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements
Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments.
To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o
Conceivable security risks and authentication techniques for smart devices
With the rapidly escalating use of smart devices and fraudulent transaction of users’ data from their devices, efficient and reliable techniques for authentication of the smart devices have become an obligatory issue. This paper reviews the security risks for mobile devices and studies several authentication techniques available for smart devices. The results from field studies enable a comparative evaluation of user-preferred authentication mechanisms and their opinions about reliability, biometric authentication and visual authentication techniques
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