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Evolving authentication design considerations for the internet of biometric things (IoBT)

Published: 01 October 2016 Publication History

Abstract

The Internet of Things (IoT) is a design implementation of embedded system design that connects a variety of devices, sensors, and physical objects to a larger connected network (e.g. the Internet) which requires human-to-human or human-to-computer interaction. While the IoT is expected to expand the user's connectivity and everyday convenience, there are serious security considerations that come into account when using the IoT for distributed authentication. Furthermore the incorporation of biometrics to IoT design brings about concerns of cost and implementing a 'user-friendly' design. In this paper, we focus on the use of electrocardiogram (ECG) signals to implement distributed biometrics authentication within an IoT system model. Our observations show that ECG biometrics are highly reliable, more secure, and easier to implement than other biometrics.

References

[1]
The design of teaching management system in universities based on biometrics identification and the internet of things technology.
[2]
ECG uniquness. http://www.physionet.org/pn3/ecgiddb/biometric.shtml, 2016.
[3]
F. Agrafioti and D. Hatzinakos. Ecg biometric analysis in cardiac irregularity conditions. Signal, Image and Video Processing, 3(4):329--343, 2009.
[4]
F. Agrafioti, D. Hatzinakos, and A. K. Anderson. Ecg pattern analysis for emotion detection. Affective Computing, IEEE Transactions on, 3(1):102--115, 2012.
[5]
L. Biel, O. Pettersson, L. Philipson, and P. Wide. Ecg analysis: a new approach in human identification. Instrumentation and Measurement, IEEE Transactions on, 50(3):808--812, 2001.
[6]
J. Brodkin. Comcast security flaw could help burglars break into homes undetected. http://arstechnica.com/security/2016/01/comcast-security/, 2016.
[7]
S. S. Burak Kantarci, Melike Erol-Kantarci. Towards secure cloud-centric internet of biometric things. In Cloud Networking (CloudNet), 2015 IEEE 4th International Conference on, 2015.
[8]
Casia. Irisv1. http://biometrics.idealtest.org, 2016.
[9]
C.-K. Chen, C.-L. Lin, C.-T. Chiang, and S.-L. Lin. Personalized information encryption using {ECG} signals with chaotic functions. Information Sciences, 193:125 -- 140, 2012.
[10]
J. Daugman. How iris recognition works. Circuits and Systems for Video Technology, IEEE Transactions on, 14(1):21--30, 2004.
[11]
J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 15(11):1148--1161, 1993.
[12]
K. Delac and M. Grgic. A survey of biometric recognition methods. In Electronics in Marine, 2004. Proceedings Elmar 2004. 46th International Symposium, pages 184--193. IEEE, 2004.
[13]
P. S. Dirk Balfanz, D. K. Smetters and H. C. Wong. Talking to strangers: Authentication in ad-hoc wireless networks, 2002, Book.
[14]
P. Ducklin. IoT security in the spotlight at PrivacyCon. https://nakedsecurity.sophos.com/2016/01/22/iot-security-in-the-spotlight-at-privacycon/, 2016.
[15]
Fitbit. Fitbit Website. https://www.fitbit.com/, 2016.
[16]
A. L. e. a. Goldberger. Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. 101(23):e215--e220, 2000.
[17]
D. Goodin. Why Algebraic Eraser may be the riskiest cryptosystem you've never heard of. http://arstechnica.com/security/2015/11/why-algebraiceraser-maybe-the-most-risky-cryptosystem/, 2015.
[18]
Z. Guo, N. Karimian, M. Tehranipoor, and D. Forte. Hardware security meets biometrics for the age of iot. In IEEE International Symposium on Circuits and Systems (ISCAS), 2016.
[19]
N. Inc. nymi - white paper. 2015.
[20]
S. M. M. P. Jayavardhana Gubbi, Rajkumar Buyya. Internet of things (iot): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29:1645--1660, February 2013.
[21]
N. Karimian, Z. Guo, M. Tehranipoor, and D. Forte. Highly reliable key generation from electrocardiogram (ecg). 2016.
[22]
S. Li and T. Tryfonas. The internet of things: a security point of view. Internet Research, pages 1--34, 2015.
[23]
I. LÃijtkebohle. Gartner's hype cycle special report for 2011. http://www.gartner.com/technology/research/hype-cycles, 2011.
[24]
K. Monks. The guns that know who is firing them: Can smart tech make firearms safer?, 2014.
[25]
H. Ning, H. Liu, et al. Cyber-physical-social based security architecture for future internet of things. Advances in Internet of Things, 2(01):1, 2012.
[26]
Nymi. Nymi Website. https://www.nymi.com, 2016.
[27]
I. Odinaka, P.-H. Lai, A. D. Kaplan, J. A. O'Sullivan, E. J. Sirevaag, and J. W. Rohrbaugh. Ecg biometric recognition: A comparative analysis. Information Forensics and Security, IEEE Transactions on, 7(6):1812--1824, 2012.
[28]
D. W. Osten, H. M. Carim, M. R. Arneson, and B. L. Blan. Biometric, personal authentication system, Feb. 17 1998. US Patent 5,719,950.
[29]
J. Pan and W. J. Tompkins. A real-time qrs detection algorithm. Biomedical Engineering, IEEE Transactions on, (3):230--236, 1985.
[30]
A. Riahi, Y. Challal, E. Natalizio, Z. Chtourou, and A. Bouabdallah. A systemic approach for iot security. In Distributed Computing in Sensor Systems (DCOSS), 2013 IEEE International Conference on, pages 351--355. IEEE, 2013.
[31]
A. Rinaldi. Biometrics' new identity - measuring more physical and biological traits. EMBO reports, 17(1):22--26, 2016.
[32]
A. Rukhin, J. Soto, J. Nechvatal, M. Smid, and E. Barker. A statistical test suite for random and pseudorandom number generators for cryptographic applications. Technical report, DTIC Document, 2001.
[33]
Sense. Mother Website. https://sen.se/mother/, 2016.
[34]
F. Sufi, I. Khalil, and J. Hu. Ecg-based authentication. In Handbook of Information and Communication Security, pages 309--331. Springer, 2010.
[35]
M. Sujithra and G. Padmavathi. Iot security challenges and issues---an overview. Avinashilingam, 2016.
[36]
F. Tehranipoor, N. Karimian, K. Xiao, and J. Chandy. Dram based intrinsic physical unclonable functions for system level security. In Proceedings of the 25th Edition on Great Lakes Symposium on VLSI, GLSVLSI '15, pages 15--20, New York, NY, USA, 2015. ACM.
[37]
F. Tehranipoor, W. Yan, and J. A. Chandy. Robust hardware true random number generators using dram remanence effects. In 2016 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pages 79--84, May 2016.
[38]
L. Vaas. IoT doorbell have up Wi-Fi passwords to anybody with a screwdriver. https://nakedsecurity.sophos.com/2016/01/27/iot-doorbell-gave-up-wi-fi-passwords-to-anybody-with\-a-screwdriver/, 2016.
[39]
L. Vaas. We might use your IoT stuff to spy on you, says top spook James Clapper. https://nakedsecurity.sophos.com/2016/02/11/we-might-use-your-iot-stuff-to-spy-on-you-says-top-spook\-james-clapper, 2016.
[40]
J. West, T. Kohno, D. Lindsay, and J. Sechman. Wearfit: Security design analysis of a wearable fitness tracker. Technical report, IEEE Center for Secure Design, February 2016.
[41]
W. Yan, F. Tehranipoor, and J. A. Chandy. A novel way to authenticate untrusted integrated circuits. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design, ICCAD '15, pages 132--138, Piscataway, NJ, USA, 2015. IEEE Press.
[42]
Z. Zorz. WiFi jamming attacks more simple and cheaper than ever. https://www.helpnetsecurity.com/2015/10/13/wifi-jamming-attacks-more-simple-and-cheaper-than-ever/, 2015.

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  • (2023)Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement LearningSensors10.3390/s2303123023:3(1230)Online publication date: 20-Jan-2023
  • (2023)ECG-based authentication systems: a comprehensive and systematic reviewMultimedia Tools and Applications10.1007/s11042-023-16506-383:9(27647-27701)Online publication date: 23-Aug-2023
  • (2023)Biometric Framework for Securing IoT EnvironmentArtificial Intelligence and Sustainable Computing10.1007/978-981-99-1431-9_51(633-649)Online publication date: 24-Sep-2023
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cover image ACM Other conferences
CODES '16: Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis
October 2016
294 pages
ISBN:9781450344838
DOI:10.1145/2968456
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 October 2016

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Author Tags

  1. ECG
  2. NIST statistical test suite
  3. authentication
  4. biometrics
  5. embedded design
  6. internet of things (IoT)
  7. reliability
  8. security

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ESWEEK'16
ESWEEK'16: TWELFTH EMBEDDED SYSTEM WEEK
October 1 - 7, 2016
Pennsylvania, Pittsburgh

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  • (2023)ECG-based authentication systems: a comprehensive and systematic reviewMultimedia Tools and Applications10.1007/s11042-023-16506-383:9(27647-27701)Online publication date: 23-Aug-2023
  • (2023)Biometric Framework for Securing IoT EnvironmentArtificial Intelligence and Sustainable Computing10.1007/978-981-99-1431-9_51(633-649)Online publication date: 24-Sep-2023
  • (2022)In-Band Secret-Free Pairing for COTS Wireless DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2020.301501021:2(612-628)Online publication date: 1-Feb-2022
  • (2022)An IoT-Oriented Privacy-Preserving Fingerprint Authentication SystemIEEE Internet of Things Journal10.1109/JIOT.2021.31319569:14(11760-11771)Online publication date: 15-Jul-2022
  • (2022)Lightweight Two-Factor-Based User Authentication Protocol for IoT-Enabled Healthcare Ecosystem in Quantum ComputingArabian Journal for Science and Engineering10.1007/s13369-022-07235-048:2(2347-2357)Online publication date: 22-Sep-2022
  • (2022)Authentication Framework for Healthcare Devices Through Internet of Things and Machine LearningEvolutionary Computing and Mobile Sustainable Networks10.1007/978-981-16-9605-3_27(399-415)Online publication date: 22-Mar-2022
  • (2021)Biometrics for Internet-of-Things Security: A ReviewSensors10.3390/s2118616321:18(6163)Online publication date: 14-Sep-2021
  • (2021)Uncertainty-aware Decisions in Cloud ComputingACM Computing Surveys10.1145/344758354:4(1-30)Online publication date: 24-May-2021
  • (2021)Trustworthy Method for Person Identification in IIoT Environments by Means of Facial DynamicsIEEE Transactions on Industrial Informatics10.1109/TII.2020.297777417:2(766-774)Online publication date: Feb-2021
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