research-article

Adaptive entity-identifier generation for IMD emergency access

Authors:

Robert M. Seepers,

Christos Strydis,

Ioannis Sourdis,

Chris I. De ZeeuwAuthors Info & Claims

CS2 '14: Proceedings of the First Workshop on Cryptography and Security in Computing Systems

Pages 41 - 44

https://doi.org/10.1145/2556315.2556324

Published: 20 January 2014 Publication History

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Abstract

Recent work on wireless Implantable Medical Devices (IMDs) has revealed the need for secure communication in order to prevent data theft and implant abuse by malicious attackers. However, security should not be provided at the cost of patient safety and an IMD should, thus, remain accessible during an emergency regardless of device security. In this paper, we present a novel method of providing IMD emergency access, based on generating Entity Identifiers (EI) using the Inter-Pulse Intervals (IPIs) of heartbeats. We evaluate the current state-of-the-art in EI-generation in terms of security and accessibility for healthy subjects with a wide range of heart rates. Subsequently, we present an adaptive EI-generation algorithm which takes the heart rate into account, maintaining an acceptable emergency-mode activation time (between 5-55.4 s) while improving security by up to 3.4x for high heart rates. Finally, we show that activating emergency mode may consume as little as 0.24μJ from the IMD battery.

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Cited By

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Catuogno LGaldi C(2024)Implantable Medical Device SecurityCryptography10.3390/cryptography80400538:4(53)Online publication date: 15-Nov-2024
https://doi.org/10.3390/cryptography8040053
Chizari HLupu E(2021)Extracting Randomness from the Trend of IPI for Cryptographic Operations in Implantable Medical DevicesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.292177318:2(875-888)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TDSC.2019.2921773
Levy-Loboda TRav-Acha MKatz ANissim N(2021)Cardio-ML: Detection of Malicious Clinical Programmings Aimed at Cardiac Implantable Electronic Devices Based on Machine Learning and a Missing Values Resemblance FrameworkArtificial Intelligence in Medicine10.1016/j.artmed.2021.102200(102200)Online publication date: Oct-2021
https://doi.org/10.1016/j.artmed.2021.102200
Show More Cited By

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Adaptive entity-identifier generation for IMD emergency access

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Using the Timing Information of Heartbeats as an Entity Identifier to Secure Body Sensor Network

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Information

Published In

CS2 '14: Proceedings of the First Workshop on Cryptography and Security in Computing Systems

January 2014

56 pages

ISBN:9781450324847

DOI:10.1145/2556315

General Chairs:
Jens Knoop
TU Vienna, Austria
,
Valentina Salapura
IBM
,
Program Chairs:
Israel Koren
University of Massachusetts Amherst
,
Gerardo Pelosi
Politecnico di Milano

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 January 2014

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CS2

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HiPEAC

CS2: Cryptography and Security in Computing Systems

January 20, 2014

Vienna, Austria

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CS2 '14 Paper Acceptance Rate 6 of 26 submissions, 23%;

Overall Acceptance Rate 27 of 91 submissions, 30%

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Cited By

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Catuogno LGaldi C(2024)Implantable Medical Device SecurityCryptography10.3390/cryptography80400538:4(53)Online publication date: 15-Nov-2024
https://doi.org/10.3390/cryptography8040053
Chizari HLupu E(2021)Extracting Randomness from the Trend of IPI for Cryptographic Operations in Implantable Medical DevicesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.292177318:2(875-888)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TDSC.2019.2921773
Levy-Loboda TRav-Acha MKatz ANissim N(2021)Cardio-ML: Detection of Malicious Clinical Programmings Aimed at Cardiac Implantable Electronic Devices Based on Machine Learning and a Missing Values Resemblance FrameworkArtificial Intelligence in Medicine10.1016/j.artmed.2021.102200(102200)Online publication date: Oct-2021
https://doi.org/10.1016/j.artmed.2021.102200
Kintzlinger MCohen ANissim NRav-Acha MKhalameizer VElovici YShahar YKatz A(2020)CardiWall: A Trusted Firewall for the Detection of Malicious Clinical Programming of Cardiac Implantable Electronic DevicesIEEE Access10.1109/ACCESS.2020.29786318(48123-48140)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2978631
Kintzlinger MNissim N(2019)Keep an Eye on Your Personal Belongings! The Security of Personal Medical Devices and Their EcosystemsJournal of Biomedical Informatics10.1016/j.jbi.2019.103233(103233)Online publication date: Jun-2019
https://doi.org/10.1016/j.jbi.2019.103233
Pirbhulal SZhang HWu WMukhopadhyay SZhang Y(2018)Heartbeats Based Biometric Random Binary Sequences Generation to Secure Wireless Body Sensor NetworksIEEE Transactions on Biomedical Engineering10.1109/TBME.2018.281515565:12(2751-2759)Online publication date: Dec-2018
https://doi.org/10.1109/TBME.2018.2815155
Wu WPirbhulal SLi G(2018)Adaptive computing-based biometric security for intelligent medical applicationsNeural Computing and Applications10.1007/s00521-018-3855-9Online publication date: 26-Nov-2018
https://doi.org/10.1007/s00521-018-3855-9
Seepers RStrydis CSourdis IDe Zeeuw C(2017)Enhancing Heart-Beat-Based Security for mHealth ApplicationsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2015.249615121:1(254-262)Online publication date: Jan-2017
https://doi.org/10.1109/JBHI.2015.2496151
Čaušević AFotouhi HLundqvist K(2017)Data Security and Privacy in Cyber‐Physical Systems for HealthcareSecurity and Privacy in Cyber‐Physical Systems10.1002/9781119226079.ch15(305-326)Online publication date: 6-Oct-2017
https://doi.org/10.1002/9781119226079.ch15
Seepers RStrydis CSourdis IZeeuw C(2015)On Using a Von Neumann Extractor in Heart-Beat-Based SecurityProceedings of the 2015 IEEE Trustcom/BigDataSE/ISPA - Volume 0110.1109/Trustcom.2015.411(491-498)Online publication date: 20-Aug-2015
https://dl.acm.org/doi/10.1109/Trustcom.2015.411
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Cited By

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Recommendations

Using the Timing Information of Heartbeats as an Entity Identifier to Secure Body Sensor Network

On Using a Von Neumann Extractor in Heart-Beat-Based Security

Wireless, Ultra-Low-Power Implantable Sensor for Chronic Bladder Pressure Monitoring

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