Abstract
Electrocardiogram (ECG) sensor is one of the most commonly available and medically important sensors in a Body Sensor Network (BSN). Compromise of the ECG sensor can have severe consequences for the user as it monitors the user’s cardiac process. In this paper, we propose an approach called SIgnal Feature-correlation-based Testing (SIFT) which is used to detect temporal alteration of ECG sensors in a BSN. The novelty of SIFT lies in the fact that it does not require redundant ECG sensors nor the subject’s historical ECG data to detect the temporal alteration. SIFT works by leveraging multiple physiological signals based on the same underlying physiological process (e.g., cardiac process) – arterial blood pressure and respiration. Analysis of our case study demonstrates promising results with \(\sim \)98% accuracy in detecting even subtle alterations in the temporal properties of an ECG signal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cai, H., Venkatasubramanian, K.K.: Detecting malicious morphological alterations of ECG signals in body sensor networks. In: Proceedings of the 14th International Conference on Information Processing in Sensor Networks, pp. 342–343. ACM (2015)
Clark, S.S., Ransford, B., Rahmati, A., Guineau, S., Sorber, J., Fu, K., Xu, W.: Wattsupdoc: power side channels to nonintrusively discover untargeted malware on embedded medical devices. In: Proceedings of USENIX Workshop on Health Information Technologies, vol. 2013 (2013)
Duk-Jin, K., Prabhakaran, B.: Motion fault detection and isolation in body sensor networks. Pervasive and Mobile Computing 7(6), 727–745 (2011)
Fink, G.: Encyclopedia of Stress, Three-Volume Set, vol. 1. Academic Press (2000)
Galzarano, S., Fortino, G., Liotta, A.: Embedded self-healing layer for detecting and recovering sensor faults in body sensor networks. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2377–2382, October 2012
Goldberger, A., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)
Kim, D.J., Suk, M.H., Prabhakaran, B.: Fault detection and isolation in motion monitoring system. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5234–5237. IEEE (2012)
Kune, D., Backes, J., Clark, S., Kramer, D., Reynolds, M., Fu, K., Kim, Y., Xu, W.: Ghost talk: mitigating EMI signal injection attacks against analog sensors. In: 2013 IEEE Symposium on Security and Privacy (SP), pp. 145–159, May 2013
Malik, M., Bigger, J.T., Camm, A.J., Kleiger, R.E., Malliani, A., Moss, A.J., Schwartz, P.J.: Heart rate variability standards of measurement, physiological interpretation, and clinical use. European heart journal 17(3), 354–381 (1996)
McSharry, P., Clifford, G., Tarassenko, L., Smith, L.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3), 289–294 (2003)
Pitzalis, M.V., Mastropasqua, F., Massari, F., Passantino, A., Colombo, R., Mannarini, A., Forleo, C., Rizzon, P.: Effect of respiratory rate on the relationships between rr interval and systolic blood pressure fluctuations: a frequency-dependent phenomenon. Cardiovascular Research 38(2), 332–339 (1998)
Sagha, H., del R Millan, J., Chavarriaga, R.: Detecting and rectifying anomalies in body sensor networks. In: 2011 International Conference on Body Sensor Networks, pp. 162–167 (2011)
Zahra, T., Mohsen, S.: A trust-based distributed data fault detection algorithm for wireless sensor networks. In: Proceedings of International Workshop on Internet and Distributed Computing System (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Cai, H., Venkatasubramanian, K.K. (2015). Detecting Malicious Temporal Alterations of ECG Signals in Body Sensor Networks. In: Qiu, M., Xu, S., Yung, M., Zhang, H. (eds) Network and System Security. NSS 2015. Lecture Notes in Computer Science(), vol 9408. Springer, Cham. https://doi.org/10.1007/978-3-319-25645-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-25645-0_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25644-3
Online ISBN: 978-3-319-25645-0
eBook Packages: Computer ScienceComputer Science (R0)