The Dual Role of Smartphones in IoT Security

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10726)


The world is entering the era of Internet of Things (IoT), where the interconnected physical devices of various forms, often embedded with electronics, software, sensors, actuators, etc., jointly perform sophisticated sensing and computing tasks and provide unprecedented services. Centering around this new paradigm is the ubiquitous smartphone. Equipped with abundant sensing, computing and networking capabilities, the smartphone is widely recognised as one of the key enablers towards IoT and the driving force that brings a great many innovative services under the way.

Despite the promising aspects, along with the rise of IoT is the increasing concerns on cybersecurity. The smartphone in this new context, however, plays a very intriguing dual role, due to the fact that it is deeply interleaved into almost every aspect of our daily living. On the one hand, it could be used as a low-cost attacking device, trying to penetrate into the scenarios that have never been considered before. On the other hand, it is also the first line of defense in the security forefront. In both cases, we need to carefully study and comprehensively understand the capability of smartphones, as well as their security implications. In this talk, we will use two examples to illustrate this observation and hopefully promote further researches along this line.


  1. 1.
  2. 2.
    Hausenblas, M.: Smart phones and the internet of things.
  3. 3.
    Song, C., Lin, F., Ba, Z., Ren, K., Zhou, C., Xu, W.: My smartphone knows what you print: exploring smartphone-based side-channel attacks against 3D printers. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 895–907. ACM (2016)Google Scholar
  4. 4.
    Chen, S., Ren, K., Piao, S., Wang, C., Wang, Q., Weng, J., Su, L., Mohaisen, A.: You can hear but you cannot steal: defending against voice impersonation attacks on smartphones. In: Proceedings of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 183–195 (2017)Google Scholar
  5. 5.
    Anderson, P., Sherman, C.A.: A discussion of new business models for 3D printing. Int. J. Technol. Mark. 2, 280–294 (2007)CrossRefGoogle Scholar
  6. 6.
    Hou, J.U., Kim, D.G., Choi, S., Lee, H.K.: 3D print-scan resilient watermarking using a histogram-based circular shift coding structure. In: Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security, pp. 115–121 (2015)Google Scholar
  7. 7.
    Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964)CrossRefGoogle Scholar
  8. 8.
    Lee, K.B., Grice, R.A.: The design and development of user interfaces for voice application in mobile devices. In: Proceedings of IEEE International Professional Communication Conference, pp. 308–320 (2006)Google Scholar
  9. 9.
    Villalba, J., Lleida, E.: Detecting replay attacks from far-field recordings on speaker verification systems. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N.C., Fairhurst, M.C. (eds.) BioID 2011. LNCS, vol. 6583, pp. 274–285. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  10. 10.
    Stylianou, Y.: Voice transformation: a survey. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3585–3588 (2009)Google Scholar
  11. 11.
    Wu, Z., Li, H.: Voice conversion and spoofing attack on speaker verification systems. In: Proceedings of IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1–9 (2013)Google Scholar
  12. 12.
    Alegre, F., Vipperla, R., Evans, N., Fauve, B.: On the vulnerability of automatic speaker recognition to spoofing attacks with artificial signals. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 36–40 (2012)Google Scholar
  13. 13.
    Evans, N., Yamagishi, J., Kinnunen, T.: Spoofing and countermeasures for speaker verification: a need for standard corpora, protocols and metrics. IEEE Signal Processing Society Speech and Language Technical Committee Newsletter, May 2013Google Scholar
  14. 14.
    Villalba, J., Lleida, E.: Preventing replay attacks on speaker verification systems. In: Proceedings of IEEE International Carnahan Conference on Security Technology (ICCST), pp. 1–8 (2011)Google Scholar
  15. 15.
    Wang, Z.F., Wei, G., He, Q.H.: Channel pattern noise based playback attack detection algorithm for speaker recognition. In: Proceedings of IEEE International Conference on Machine Learning and Cybernetics (ICMLC), vol. 4, pp. 1708–1713 (2011)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Cyber Security ResearchZhejiang UniversityHangzhouChina

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