FTM-IoMT: Fuzzy-Based Trust Management for Preventing Sybil Attacks in Internet of Medical Things

Abstract

Trustworthy transmission is a beneficial step toward the success of the new era of telecommunication technologies and online social networks (OSNs). Many sensitive applications can benefit from OSNs, e.g., eHealth and medical services. However, OSNs have always been prey to Sybil attacks where numerous fake nodes are being generated and propagated in social networks to mimic like real nodes for the purpose of achieving malicious goals. Thus, for security reasons and for the sensitivity of data used in eHealth applications, such fake nodes have to be detected and deactivated immediately. The emerging field of the Internet of Medical Things (IoMT) promotes trust management (TM) among various IoMT devices to provide accurate and reliable communications, which is quite essential in critical diseases such as COVID-19. TM provides a secure platform to IoMT devices using different security protocols in the IoMT network. Generically, if a device is not comfortable to connect with additional devices in a network, the motive of the communication process is not succeeded and leads to disappointment for one device toward others. To handle these types of situations, a TM mechanism, named fuzzy-based TM mechanism for preventing Sybil attacks in the Internet of Medical Things (FTM-IoMT), is proposed. The FTM-IoMT provides TM for the users of eHealth systems using IoMT infrastructures. It is an intelligent mechanism to recognize Sybil or untrustworthy nodes in the system. The proposed mechanism helps IoMT nodes to collect authentic and credible information from their neighboring nodes as well as to neglect Sybil nodes. The trust value of a node is evaluated using fuzzy logic processing followed by the trust attributes, such as integrity, receptivity, and compatibility of a node. The FTM-IoMT provides a double evaluation check based on fuzzy logic processing and fuzzy filter. The proposed scheme shows superior results when compared to the state-of-the-art approaches.