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On-Phone CNN Model-Based Implicit Authentication to Secure IoT Wearables

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The Fifth International Conference on Safety and Security with IoT

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The connectivity of smart technologies, such as smartphones and smart wearables, is ever-increasing with the emergence of the internet of things (IoT). This technological advancement makes it possible to serve emerging applications, such as financial transactions, healthcare check-ups, and property access, easily through smart wearables, such as Apple Watch. This also presents a new vulnerability as hackers have more opportunities to attack users via the wearables. As the current knowledge-based wearable authentication schemes, such as passwords, PINs, or pattern locks, are overwhelming for users, we need an authentication system that can validate a user implicitly, i.e., without the need for active user interaction. In this work, we present an authentication system for the wearables leveraging the sensing and computation power of smartphones and IoT connectivity. We develop a smartphone application (TFL Auth app) using the TensorFlow Lite framework and an on-phone convolutional neural network (CNN) model that listens to a user’s breathing patterns through the microphone and verifies the user’s identity in real-time before sending the acceptance/rejection notification to a paired wearable that we want to secure. From a detailed analysis, we are able to achieve an average accuracy of 0.92 ± 0.01 using the Mel-frequency cepstral coefficients.

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Author information

Authors and Affiliations

  1. Dartmouth College, Hanover, NH, USA

    Sayanton V. Dibbo

  2. Purdue University, West Lafayette, IN, USA

    William Cheung & Sudip Vhaduri

Authors
  1. Sayanton V. Dibbo
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  2. William Cheung
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  3. Sudip Vhaduri
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Corresponding author

Correspondence to Sudip Vhaduri .

Editor information

Editors and Affiliations

  1. School of Computer Science, Duy Tan University, Da Nang, Vietnam

    Anand Nayyar

  2. The School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea (Republic of)

    Anand Paul

  3. Computer Science and Engineering, Nirma University, Gujarat, India

    Sudeep Tanwar

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Dibbo, S.V., Cheung, W., Vhaduri, S. (2023). On-Phone CNN Model-Based Implicit Authentication to Secure IoT Wearables. In: Nayyar, A., Paul, A., Tanwar, S. (eds) The Fifth International Conference on Safety and Security with IoT . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-94285-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-94285-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94284-7

  • Online ISBN: 978-3-030-94285-4

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