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Breathing-Based Authentication on Resource-Constrained IoT Devices using Recurrent Neural Networks.

Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice command...

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Published in:Computer (00189162) Vol. 51; no. 5; pp. 60 - 68
Main Authors: Chauhan, Jagmohan, Seneviratne, Suranga, Hu, Yining, Misra, Archan, Seneviratne, Aruna, Lee, Youngki
Format: Article
Published: IEEE May2018
Subjects:
Computer access control
Internet of things
Artificial neural networks
Machine learning
Constraint satisfaction
Online Access:View this record in EBSCOhost
Summary:Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored. The authors compare traditional machine-learning models with deep-learning RNN models for an end-to-end authentication system based on breathing acoustics.