TY - GEN
T1 - PIPAC
T2 - 32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013
AU - Hei, Xiali
AU - Du, Xiaojiang
AU - Lin, Shan
AU - Lee, Insup
PY - 2013
Y1 - 2013
N2 - Wireless insulin pumps have been widely deployed in hospitals and home healthcare systems. Most of these insulin pump systems have limited security mechanisms embedded to protect them from malicious attacks. In this paper, two attacks against insulin pump systems via wireless links are investigated: a single acute overdose with a significant amount of medication, and chronic overdose with an insignificant amount of extra medication over a long time period, e.g., several months. These attacks can be launched unobtrusively and may jeopardize patients' lives. It is very important and urgent to protect patients from these attacks. To address this issue, we propose a novel patient infusion pattern based access control scheme (PIPAC) for wireless insulin pumps. This scheme employs a supervised learning approach to learn normal patient infusions pattern with the dosage amount, rate, and time of infusion, which are automatically recorded in insulin pump logs. The generated regression models are used to dynamically configure a safety infusion range for abnormal infusion identification. The proposed algorithm is evaluated with real insulin pump logs used by several patients for up to 6 months. The evaluation results demonstrate that our scheme can reliably detect the single overdose attack with a success rate up to 98% and defend against the chronic overdose attack with a very high success rate.
AB - Wireless insulin pumps have been widely deployed in hospitals and home healthcare systems. Most of these insulin pump systems have limited security mechanisms embedded to protect them from malicious attacks. In this paper, two attacks against insulin pump systems via wireless links are investigated: a single acute overdose with a significant amount of medication, and chronic overdose with an insignificant amount of extra medication over a long time period, e.g., several months. These attacks can be launched unobtrusively and may jeopardize patients' lives. It is very important and urgent to protect patients from these attacks. To address this issue, we propose a novel patient infusion pattern based access control scheme (PIPAC) for wireless insulin pumps. This scheme employs a supervised learning approach to learn normal patient infusions pattern with the dosage amount, rate, and time of infusion, which are automatically recorded in insulin pump logs. The generated regression models are used to dynamically configure a safety infusion range for abnormal infusion identification. The proposed algorithm is evaluated with real insulin pump logs used by several patients for up to 6 months. The evaluation results demonstrate that our scheme can reliably detect the single overdose attack with a success rate up to 98% and defend against the chronic overdose attack with a very high success rate.
KW - access control
KW - implantable medical devices
KW - infusion pattern
KW - patient safety
KW - wireless insulin pump
UR - http://www.scopus.com/inward/record.url?scp=84883076209&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883076209&partnerID=8YFLogxK
U2 - 10.1109/INFCOM.2013.6567115
DO - 10.1109/INFCOM.2013.6567115
M3 - Conference contribution
AN - SCOPUS:84883076209
SN - 9781467359467
T3 - Proceedings - IEEE INFOCOM
SP - 3030
EP - 3038
BT - 2013 Proceedings IEEE INFOCOM 2013
Y2 - 14 April 2013 through 19 April 2013
ER -