TY - GEN
T1 - Enhancing Trust-based Medical Smartphone Networks via Blockchain-based Traffic Sampling
AU - Li, Wenjuan
AU - Meng, Weizhi
AU - Yang, Laurence T.
N1 - Funding Information:
This work was partially supported by the National Natural Science Foundation of China (No. 61802077). The authors would like to thank the support and help from the participating organization.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/10
Y1 - 2021/10
N2 - With more devices being inter- or intra-connected, Internet of Things (IoT) has gradually been adopted in many disciplines, such as healthcare industry, coined as Internet of Medical Things (IoMT). The purpose of IoMT is to facilitate the efficiency and effectiveness of medical operations, i.e., remotely monitoring the status of patients. In such healthcare environments, smartphones have become an important device to communicate with others and update the information of patients, resulting in a special type of IoMT called Medical Smartphone Networks (MSNs). To reinforce the distributed architecture, trust management schemes are often implemented to defend against insider attacks. However, how to maintain the robustness of trust management in heavy traffic networks still remains a challenge, i.e., COVID-19 incident would cause excessive traffic for healthcare organizations and increase the difficulty of validating trustworthiness among MSN nodes. In this work, we focus on this issue and propose a blockchain-enabled adaptive traffic sampling method to help enhance the robustness of trust management under high traffic environments. The use of blockchain technology aims to build a verified database of malicious traffic among all nodes. The evaluation in a real healthcare environment demonstrates the viability and effectiveness of our approach.
AB - With more devices being inter- or intra-connected, Internet of Things (IoT) has gradually been adopted in many disciplines, such as healthcare industry, coined as Internet of Medical Things (IoMT). The purpose of IoMT is to facilitate the efficiency and effectiveness of medical operations, i.e., remotely monitoring the status of patients. In such healthcare environments, smartphones have become an important device to communicate with others and update the information of patients, resulting in a special type of IoMT called Medical Smartphone Networks (MSNs). To reinforce the distributed architecture, trust management schemes are often implemented to defend against insider attacks. However, how to maintain the robustness of trust management in heavy traffic networks still remains a challenge, i.e., COVID-19 incident would cause excessive traffic for healthcare organizations and increase the difficulty of validating trustworthiness among MSN nodes. In this work, we focus on this issue and propose a blockchain-enabled adaptive traffic sampling method to help enhance the robustness of trust management under high traffic environments. The use of blockchain technology aims to build a verified database of malicious traffic among all nodes. The evaluation in a real healthcare environment demonstrates the viability and effectiveness of our approach.
KW - Blockchain
KW - Insider Threat
KW - Internet of Medical Things
KW - Medical Smartphone Networks
KW - Traffic Sampling
UR - http://www.scopus.com/inward/record.url?scp=85125609050&partnerID=8YFLogxK
U2 - 10.1109/TrustCom53373.2021.00034
DO - 10.1109/TrustCom53373.2021.00034
M3 - Conference article published in proceeding or book
AN - SCOPUS:85125609050
T3 - Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
SP - 122
EP - 129
BT - Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
A2 - Zhao, Liang
A2 - Kumar, Neeraj
A2 - Hsu, Robert C.
A2 - Zou, Deqing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
Y2 - 20 October 2021 through 22 October 2021
ER -