TY - GEN
T1 - Detecting malicious nodes in medical smartphone networks through euclidean distance-based behavioral profiling
AU - Meng, Weizhi
AU - Li, Wenjuan
AU - Wang, Yu
AU - Au, Man Ho
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - With the increasing digitization of the healthcare industry, a wide range of medical devices are Internet- and inter-connected. Mobile devices (e.g., smartphones) are one common facility used in the healthcare industry to improve the quality of service and experience for both patients and healthcare personnel. The underlying network architecture to support such devices is also referred to as medical smartphone networks (MSNs). Similar to other networks, MSNs also suffer from various attacks like insider attacks (e.g., leakage of sensitive patient information by a malicious insider). In this work, we focus on MSNs and design a trust-based intrusion detection approach through Euclidean distance-based behavioral profiling to detect malicious devices (or called nodes). In the evaluation, we collaborate with healthcare organizations and implement our approach in a real simulated MSN environment. Experimental results demonstrate that our approach is promising in effectively identifying malicious MSN nodes.
AB - With the increasing digitization of the healthcare industry, a wide range of medical devices are Internet- and inter-connected. Mobile devices (e.g., smartphones) are one common facility used in the healthcare industry to improve the quality of service and experience for both patients and healthcare personnel. The underlying network architecture to support such devices is also referred to as medical smartphone networks (MSNs). Similar to other networks, MSNs also suffer from various attacks like insider attacks (e.g., leakage of sensitive patient information by a malicious insider). In this work, we focus on MSNs and design a trust-based intrusion detection approach through Euclidean distance-based behavioral profiling to detect malicious devices (or called nodes). In the evaluation, we collaborate with healthcare organizations and implement our approach in a real simulated MSN environment. Experimental results demonstrate that our approach is promising in effectively identifying malicious MSN nodes.
KW - Collaborative network
KW - Insider attack
KW - Intrusion detection
KW - Malicious node
KW - Medical Smartphone Network
KW - Trust computation and management
UR - http://www.scopus.com/inward/record.url?scp=85034247540&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69471-9_12
DO - 10.1007/978-3-319-69471-9_12
M3 - Conference article published in proceeding or book
AN - SCOPUS:85034247540
SN - 9783319694702
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 163
EP - 175
BT - Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings
A2 - Wu, Wei
A2 - Castiglione, Aniello
A2 - Wen, Sheng
PB - Springer Verlag
T2 - 9th International Symposium on Cyberspace Safety and Security, CSS 2017
Y2 - 23 October 2017 through 25 October 2017
ER -