TY - JOUR
T1 - DLRT
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
AU - Rathore, Heena
AU - Al-Ali, Abdulla
AU - Mohamed, Amr
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. In addition, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.
AB - Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. In addition, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.
KW - Deep learning
KW - Implantable medical devices
KW - Insulin pumps
KW - Machine learning
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85046397222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046397222&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2017.8255028
DO - 10.1109/GLOCOM.2017.8255028
M3 - Conference article
AN - SCOPUS:85046397222
SN - 2334-0983
VL - 2018-January
SP - 1
EP - 6
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
Y2 - 4 December 2017 through 8 December 2017
ER -