Fuzzy-based Misbehavior Detection for Internet of Things in Multi-access Edge Computing Environment

Today, an emerging paradigm of edge computing, called Multi-Access Edge Computing (MEC), is widely used in a broad range of applications. MEC has been recently used as a driving technology for 5G net-works. However, MEC encompasses some security problems, such as communicating with untrusted third-party service providers commonly known as location-based service (LBS) providers. In this paper, we propose a novel fuzzy-based misbehavior detection system for Internet of Things (IoT) in a 5G-based MEC environment to solve the previous problems. Our proposed system is comprised of a multi-tier architecture that is linked by IoT gateways and mobile core network. In our architecture, we employ a novel mechanism that embeds a security entity called Regional Certificate Authority in a 5G macro base station. This security entity acts as a passport for the IoT gateway to communicate with other security entities present in our system that are located in the core infrastructure. Besides that, our system utilizes a distributed certificate revocation mechanism for malicious LBS providers via simultaneously updating the internal blocklists employed by more than one system entity. In addition, we adopt in our architec-ture a novel penalty system for malicious LBS providers, which is used for penalizing providers in case proved to be malicious as well as actively isolating them from the network. On the other hand, our system provides conditional anonymity for the incident's reporter. Also, we present various security features maintained by the proposed system to thwart well-known attacks as well as we demonstrate efficiency and robustness of our system. Finally, we provide the overhead of our system and compare it with other state-of-the-art approaches where results show that the proposed system relatively incurs the least overhead.& COPY; 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).