Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Lee, Wei-Han; Lee, Ruby B.

doi:10.1109/dsn.2017.24

Cited by 59 publications

(69 citation statements)

References 29 publications

(46 reference statements)

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“…Modern mobile devices provide a rich set of sources for extracting user behavioral data. The ubiquitous touchscreen and a variety of sensors such as audio sensors, light sensors, accelerometers, gyroscope, and magnetometers can be leveraged for capturing user behavior data [47,10,46,14,15,54,55,26,34,41,35]. Most of such work, however, has focused on user authentication as described below.…”

Section: Related Workmentioning

confidence: 99%

Kid on the phone! Toward automatic detection of children on mobile devices

Nguyen

Roy

Memon

2019

Computers & Security

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Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children-computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while operating a touch-enabled modern computing device. Behavioral differences are extracted from data recorded by the touchscreen and built-in sensors.To evaluate the effectiveness of the proposed methods, a new data set has been created from 50 children and adults who interacted with off-the-shelf applications on smart phones. Results show that it is possible to achieve 99% accuracy and less than 0.5% error rate after 8 consecutive touch gestures using only touch information or 5 seconds of sensor reading. If information is used from multiple sensors, then only after 3 gestures, similar performance could be achieved.

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Section: Related Workmentioning

confidence: 99%

Kid on the phone! Toward automatic detection of children on mobile devices

Nguyen

Roy

Memon

2019

Computers & Security

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“…In terms of the sensorial data at stake, it is remarkable that almost all precedent works [ 9 , 10 , 11 , 12 , 31 , 32 , 33 , 34 , 35 ] rely upon some external element such as GPS satellites or telephony cell towers. This issue is relevant as the absence of these elements would make their approach to be affected.…”

Section: Related Workmentioning

confidence: 99%

Secure and Usable User-in-a-Context Continuous Authentication in Smartphones Leveraging Non-Assisted Sensors

Fuentes

González-Manzano

Ribagorda

2018

Sensors

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Smartphones are equipped with a set of sensors that describe the environment (e.g., GPS, noise, etc.) and their current status and usage (e.g., battery consumption, accelerometer readings, etc.). Several works have already addressed how to leverage such data for user-in-a-context continuous authentication, i.e., determining if the porting user is the authorized one and resides in his regular physical environment. This can be useful for an early reaction against robbery or impersonation. However, most previous works depend on assisted sensors, i.e., they rely upon immutable elements (e.g., cell towers, satellites, magnetism), thus being ineffective in their absence. Moreover, they focus on accuracy aspects, neglecting usability ones. For this purpose, in this paper, we explore the use of four non-assisted sensors, namely battery, transmitted data, ambient light and noise. Our approach leverages data stream mining techniques and offers a tunable security-usability trade-off. We assess the accuracy, immediacy, usability and readiness of the proposal. Results on 50 users over 24 months show that battery readings alone achieve 97.05% of accuracy and 81.35% for audio, light and battery all together. Moreover, when usability is at stake, robbery is detected in 100 s for the case of battery and in 250 s when audio, light and battery are applied. Remarkably, these figures are obtained with moderate training and storage needs, thus making the approach suitable for current devices.

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“…Although these two commonly used methods (knowledge-based authentication and static biometric-based authentication) have been in full swing for decades, there are a series of deficiencies in usability, security, and effectiveness. Therefore, in recent years, using motion sensors such as acceleration sensors, gyro sensors, and gravity sensors as data sources, a method of authentication based on the user’s dynamic behavior, has been proposed by many researchers (e.g., gait authentication [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], user’s usage behavior authentication [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ], and daily life behavior authentication [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]). These methods have better user experience and stronger privacy protection capabilities, laying the foundation for the development of the third phase of mobile user authentication.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Zhu

Weng

Guo-lang

et al. 2020

Sensors

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With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

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Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Cited by 59 publications

References 29 publications

Kid on the phone! Toward automatic detection of children on mobile devices

Kid on the phone! Toward automatic detection of children on mobile devices

Secure and Usable User-in-a-Context Continuous Authentication in Smartphones Leveraging Non-Assisted Sensors

A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

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