Detecting data manipulation attacks on physiological sensor measurements in wearable medical systems

Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall well-being. Ensuring that these WMS accurately monitor a user’s current health state is crucial. This is especially true in the presence of adversari...

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Vydáno v:	EURASIP Journal on Information Security Ročník 2018; číslo 1; s. 1 - 21
Hlavní autoři:	Cai, Hang, Venkatasubramanian, Krishna K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 29.09.2018 Springer Nature B.V SpringerOpen
Témata:	Blood pressure Classifiers Communications Engineering Data manipulation attacks Data-centric attack detection Decision analysis Electrocardiography Engineering Health Image reconstruction Medical electronics Medical imaging Networks Physiology Pressure measurement Security Science and Technology Sensors Signal processing Signal,Image and Speech Processing Support systems Systems and Data Security Trustworthiness Wearable medical systems Wearable technology Wearable medical systems Data-centric attack detection Data manipulation attacks
ISSN:	2510-523X, 1687-4161, 2510-523X, 1687-417X
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Abstract	Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall well-being. Ensuring that these WMS accurately monitor a user’s current health state is crucial. This is especially true in the presence of adversaries who want to mount data manipulation attacks on the WMS. The goal of data manipulation attacks is to alter the measurements made by the sensors in the WMS with fictitious data that is plausible but not accurate. Such attacks force clinicians or any decision support system AI, analyzing the WMS data, to make incorrect diagnosis and treatment decisions about the patient’s health. In this paper, we present an approach to detect data manipulation attacks based on the idea that multiple physiological signals based on the same underlying physiological process (e.g., cardiac process) are inherently related to each other. We capture the commonalities between a “target” sensor measurement and another “reference” sensor measurement (which is trustworthy), by building an image reconstruction-based classifier and using this classifier to identify any unilateral changes in the target sensor measurements. This classifier is user-specific and needs to be created for every user on whom the WMS is deployed. In order to showcase our idea, we present a case study where we detect data manipulation attacks on electrocardiogram (ECG) sensor measurements in a WMS using blood pressure measurement as reference. We chose ECG and blood pressure—in arterial blood pressure (ABP) form—because both are some of the most commonly measured physiological signals in a WMS environment. Our approach demonstrates promising results with above 98% accuracy in detecting even subtle ECG alterations for both healthy subjects and those with different cardiac ailments. Finally, we show that the approach is general in that it can be used to build a model for detecting data manipulation attacks that alter ABP sensor measurements using the ECG sensor as reference.
AbstractList	Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall well-being. Ensuring that these WMS accurately monitor a user’s current health state is crucial. This is especially true in the presence of adversaries who want to mount data manipulation attacks on the WMS. The goal of data manipulation attacks is to alter the measurements made by the sensors in the WMS with fictitious data that is plausible but not accurate. Such attacks force clinicians or any decision support system AI, analyzing the WMS data, to make incorrect diagnosis and treatment decisions about the patient’s health.In this paper, we present an approach to detect data manipulation attacks based on the idea that multiple physiological signals based on the same underlying physiological process (e.g., cardiac process) are inherently related to each other. We capture the commonalities between a “target” sensor measurement and another “reference” sensor measurement (which is trustworthy), by building an image reconstruction-based classifier and using this classifier to identify any unilateral changes in the target sensor measurements. This classifier is user-specific and needs to be created for every user on whom the WMS is deployed. In order to showcase our idea, we present a case study where we detect data manipulation attacks on electrocardiogram (ECG) sensor measurements in a WMS using blood pressure measurement as reference. We chose ECG and blood pressure—in arterial blood pressure (ABP) form—because both are some of the most commonly measured physiological signals in a WMS environment. Our approach demonstrates promising results with above 98% accuracy in detecting even subtle ECG alterations for both healthy subjects and those with different cardiac ailments. Finally, we show that the approach is general in that it can be used to build a model for detecting data manipulation attacks that alter ABP sensor measurements using the ECG sensor as reference. Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall well-being. Ensuring that these WMS accurately monitor a user’s current health state is crucial. This is especially true in the presence of adversaries who want to mount data manipulation attacks on the WMS. The goal of data manipulation attacks is to alter the measurements made by the sensors in the WMS with fictitious data that is plausible but not accurate. Such attacks force clinicians or any decision support system AI, analyzing the WMS data, to make incorrect diagnosis and treatment decisions about the patient’s health. In this paper, we present an approach to detect data manipulation attacks based on the idea that multiple physiological signals based on the same underlying physiological process (e.g., cardiac process) are inherently related to each other. We capture the commonalities between a “target” sensor measurement and another “reference” sensor measurement (which is trustworthy), by building an image reconstruction-based classifier and using this classifier to identify any unilateral changes in the target sensor measurements. This classifier is user-specific and needs to be created for every user on whom the WMS is deployed. In order to showcase our idea, we present a case study where we detect data manipulation attacks on electrocardiogram (ECG) sensor measurements in a WMS using blood pressure measurement as reference. We chose ECG and blood pressure—in arterial blood pressure (ABP) form—because both are some of the most commonly measured physiological signals in a WMS environment. Our approach demonstrates promising results with above 98% accuracy in detecting even subtle ECG alterations for both healthy subjects and those with different cardiac ailments. Finally, we show that the approach is general in that it can be used to build a model for detecting data manipulation attacks that alter ABP sensor measurements using the ECG sensor as reference. Abstract Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall well-being. Ensuring that these WMS accurately monitor a user’s current health state is crucial. This is especially true in the presence of adversaries who want to mount data manipulation attacks on the WMS. The goal of data manipulation attacks is to alter the measurements made by the sensors in the WMS with fictitious data that is plausible but not accurate. Such attacks force clinicians or any decision support system AI, analyzing the WMS data, to make incorrect diagnosis and treatment decisions about the patient’s health. In this paper, we present an approach to detect data manipulation attacks based on the idea that multiple physiological signals based on the same underlying physiological process (e.g., cardiac process) are inherently related to each other. We capture the commonalities between a “target” sensor measurement and another “reference” sensor measurement (which is trustworthy), by building an image reconstruction-based classifier and using this classifier to identify any unilateral changes in the target sensor measurements. This classifier is user-specific and needs to be created for every user on whom the WMS is deployed. In order to showcase our idea, we present a case study where we detect data manipulation attacks on electrocardiogram (ECG) sensor measurements in a WMS using blood pressure measurement as reference. We chose ECG and blood pressure—in arterial blood pressure (ABP) form—because both are some of the most commonly measured physiological signals in a WMS environment. Our approach demonstrates promising results with above 98% accuracy in detecting even subtle ECG alterations for both healthy subjects and those with different cardiac ailments. Finally, we show that the approach is general in that it can be used to build a model for detecting data manipulation attacks that alter ABP sensor measurements using the ECG sensor as reference.
ArticleNumber	13
Author	Cai, Hang Venkatasubramanian, Krishna K.
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Cites_doi	10.1109/CIC.2015.7411064 10.1161/01.CIR.101.23.e215 10.1109/ICSMC.2012.6378098 10.1109/MPRV.2008.16 10.1016/j.imavis.2007.03.004 10.1002/gepi.20211 10.1109/TBME.2003.808805 10.1016/j.pmcj.2011.09.006 10.1109/SP.2013.20 10.3390/s90201282 10.1109/TIE.2010.2070772 10.1016/0169-7439(87)80084-9 10.1109/DCOSS.2016.36 10.1007/978-3-540-36675-1_15 10.1109/ISIE.2007.4374812 10.1016/j.comcom.2008.06.014 10.3109/03091900903150998 10.1007/978-3-319-03005-0_54 10.1109/CNS.2014.6997498
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Snippet	Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall... Abstract Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall...
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SubjectTerms	Blood pressure Classifiers Communications Engineering Data manipulation attacks Data-centric attack detection Decision analysis Electrocardiography Engineering Health Image reconstruction Medical electronics Medical imaging Networks Physiology Pressure measurement Security Science and Technology Sensors Signal processing Signal,Image and Speech Processing Support systems Systems and Data Security Trustworthiness Wearable medical systems Wearable technology
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Title	Detecting data manipulation attacks on physiological sensor measurements in wearable medical systems
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