Internet of Things (IoT) has gradually become one of the most important platforms across different disciplines, by enabling dedicated physical objects to communicate with other Internet-enabled things. With this trend, more devices in medical environments are capable of connecting with each other, named Internet of Medical Things (IoMT). It aims for improving efficiency and reducing communication delay, e.g., monitoring the status of patients and notifying abnormal events. However, due to the distributed nature, insider attacks are still one of the major threats to such IoT environment. How to improve the trust management in IoMT remains a challenge. Motivated by the popularity of blockchain technology, in this paper, our general goal is to investigate the performance of blockchain-based trust management. In particular, we focus on a particular type of IoMT, named medical smartphone networks (MSNs), because of the wide adoption of smartphones in the medical domain. Then, we apply blockchains for enhancing the effectiveness of Bayesian inference-based trust management to detect malicious nodes in MSNs. In the evaluation, we explore the performance of our approach in two different healthcare environments, and experimental results demonstrate that blockchain technology can help improve the detection efficiency of detecting malicious nodes with reasonable workload.

Owing to the swift growth in cyber attacks, intrusion detection systems (IDSs) have become a necessity to help safeguard personal and organizational assets. However, with the increasing size of computer networks, it becomes difficult for a stand-alone IDS to identify sophisticated and advanced threats, such as DDoS attack, due to the lack of contextual information and knowledge regarding the deployed environments. To tackle this issue, distributed and collaborative IDSs (DIDSs and CIDSs) are developed, which enable a set of IDS nodes to operate in a collaborative way through exchanging required information. In this survey, we first summarize the state-of-the-art for traditional DIDSs according to the collaboration topology, e.g., centralized, decentralized, and distributed, and discuss major external and internal threats. Because of the distributed nature and various threats, trust is often enforced among various IDS nodes. We then summarize the relevant research on trust-based DIDSs/CIDSs in a chronological order. Also, we highlight challenges and future directions in this field. The main purpose of this survey is to stimulate more research efforts in developing robust and practical trust-based collaborative intrusion detection.