Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features

Deb, Debayan; Ross, Arun; Jain, Anil K.; Prakah-Asante, Kwaku O.; Prasad, K. Venkatesh

doi:10.1109/icb45273.2019.8987433

Cited by 40 publications

(38 citation statements)

References 35 publications

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“…Future work will be oriented to study the discriminative power of new features acquired in MobileTouchDB such as the area covered by the finger, accelerometer, and gyroscope in order to further improve the system performance [36]. We will also analyse the user experience in different acquisition settings through the analysis of the information acquired from Block5 to Block8 of the MobileTouchDB.…”

Section: Discussionmentioning

confidence: 99%

BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

Tolosana

Vera-Rodríguez

Fiérrez

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) 1 . This approach combines the potential of DTW and RNNs to train more robust systems against attacks.A complete analysis of the proposed approach is carried out using both MobileTouchDB 2 and e-BioDigitDB 3 databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario.

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Section: Discussionmentioning

confidence: 99%

BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

Tolosana

Vera-Rodríguez

Fiérrez

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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“…Each hidden layer of LSTM is recursively connected by a series of memory blocks, one memory block corresponds to a memory unit, and each memory unit contains three gates: input gate, output gate, and forgetting gate, which can read, write, and reset the memory unit, respectively, so as to flexibly control the information transmission between different memory units. The state of LSTM memory unit is updated by the following formula [29,30]:…”

Section: Long-time Extract Based On Lstmmentioning

confidence: 99%

Prediction of Behavior and Activity of Patients through Wearable Devices Based on Deep Learning

Zhang

Zhao

2022

Journal of Sensors

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With the aging problem becoming more and more serious, muscle pain has become a common symptom. In order to help patients with rehabilitation training, it is necessary to monitor their activities in time. We propose a real-time monitoring method based on wearable devices. This method uses a wireless body area network for health care. Specifically, in the first step, we developed a wearable device based on ZigBee with low cost and low weight. Secondly, if only classifying the action at the current time, it will not meet the requirements of real-time monitoring. So, we design an end-to-end neural network model called ATCRNN to infer the actions to be made by users at the next time according to the data of the past few times. This model uses CNN and RNN to extract the spatial and temporal features of data and captures the context characteristics through self-attention. Finally, four volunteers wore equipment to participate in the experiment. The activity categories in the experiment are walking, sitting down, running, and climbing stairs. The accuracy of behavior inference reached 97% with ATCRNN. The compared results demonstrate that the performance of the proposed approach is superior to the result of other deep learning networks.

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“…Most of the literature, in general, addresses one or two modalities isolated from other modalities, i.e., these approaches are suitable for only one or two specific situations. Recently, the proposed studies [ 172 , 173 ] utilized multiple modalities and evaluated their approaches on different modes. Further work in this direction combined with privacy-preserving approaches can solve modality-specific and privacy issues.…”

Section: Recommendations For Future Research Directionsmentioning

confidence: 99%

Security, Privacy, and Usability in Continuous Authentication: A Survey

Baig

Eskeland

2021

Sensors

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Continuous authentication has been proposed as a possible approach for passive and seamless user authentication, using sensor data comprising biometric, behavioral, and context-oriented characteristics. Since these are personal data being transmitted and are outside the control of the user, this approach causes privacy issues. Continuous authentication has security challenges concerning poor matching rates and susceptibility of replay attacks. The security issues are mainly poor matching rates and the problems of replay attacks. In this survey, we present an overview of continuous authentication and comprehensively discusses its different modes, and issues that these modes have related to security, privacy, and usability. A comparison of privacy-preserving approaches dealing with the privacy issues is provided, and lastly recommendations for secure, privacy-preserving, and user-friendly continuous authentication.

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Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features

Cited by 40 publications

References 35 publications

BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

Prediction of Behavior and Activity of Patients through Wearable Devices Based on Deep Learning

Security, Privacy, and Usability in Continuous Authentication: A Survey

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