A4 Vertaisarvioitu artikkeli konferenssijulkaisussa

Cryptographic key generation using ECG signal




TekijätMoosavi Sanaz Rahimi, Nigussie Ethiopia, Virtanen Seppo, Isoaho Jouni

Konferenssin vakiintunut nimiIEEE Annual Consumer Communications & Networking Conference (CCNC)

KustantajaInstitute of Electrical and Electronics Engineers Inc.

Julkaisuvuosi2017

Tietokannassa oleva lehden nimi2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017

Sarjan nimiIEEE Annual Consumer Communications & Networking Conference (CCNC)

Aloitussivu1024

Lopetussivu1031

Sivujen määrä8

ISBN978-1-5090-6197-6

eISBN978-1-5090-6196-9

ISSN2331-9860

DOIhttps://doi.org/10.1109/CCNC.2017.7983280


Tiivistelmä

n this paper, two different electrocardiogram (ECG) based cryptographic key generation approaches are proposed. The aim is to enhance the security of body area networks through robust key generation where keys are generated on the fly without requiring key pre-distribution solutions. The Interpulse Interval (IPI) feature of ECG underlays both of the proposed approaches. The first approach is realized by using a pseudo-random number and consecutive IPI sequences. The second approach is realized by utilizing the Advanced Encryption Standard (AES) algorithm and IPI as the seed generator for the AES algorithm. The efficiency of the proposed approaches is evaluated using real ECG data of 15 patients obtained from the MIT-BIH Arrhythmia dataset of PhysioBank. The security analyses of the generated keys are carried out in terms of distinctiveness, randomness, and temporal variance as well as using the NIST benchmark. The analyses show that our key generation approaches provide a higher security level in comparison to existing approaches relying only on singleton IPI sequences. The execution times required to generate the cryptographic keys on different processors are also examined. The results reveal that the security level improvement comes with a reasonable increase in key generation execution time. Comparing to existing IPI-based approaches, our approaches require 12.3% and 41.2% more execution time, respectively.



Last updated on 2024-26-11 at 14:25