Hybrid Trust-based Defense Mechanisms Against Sybil Attack in Vehicular Ad-hoc Networks

Abstract

The application of IoT in Vehicular Ad-Hoc Networks (VANET) allows the realization of intelligent transportation systems to ensure the comfort and safety of road users. However, as an implication, attacks that can interfere with this aim certainly need to be a significant concern. It is because the stakes are not only network security but also the safety of the driver and its passengers. One of the attacks which had a substantial impact on VANET was the Sybil attack. Sybil attackers illegally change into several different identities to carry out malicious activities such as disrupting routing, causing traffic jams, bottlenecks, and even accidents. Various security methods are introduced to VANET to detect Sybil attacks, but there are still several issues that have not been resolved. In this paper, we proposed the design of a defense mechanism against Sybil attacks. This mechanism aims to be suitable for application on IoT networks that have limited resources an also considering accuracy, privacy, safety, and real-world implementations. The proposed design uses a hybrid scheme with a trust-based method. Each node has an obfuscated identity to guarantee privacy. The trust center in the form of a Road Side Unit (RSU) will give a reputation value to each identity that will be evaluated periodically when in the range. Nodes form a fully distributed network when there is no RSU. It will use a data-centric neighbor trust scheme where its neighbors will assess each node based on the exchanging data. Each node reports on suspicious nodes to the RSU for evaluation. This mechanism allows RSU to evaluate suspicious node, which decides to isolate that Sybil node out of network.

Attack Classification and Detection for Misbehaving Vehicles using ML/DL

“…Kumar Sharshembiev et al, [20] proposed the detection of protocol misbehavior is discussed utilizing cutting-edge machine learning frameworks and entropy. In order to detect misbehavior in VANETs, they investigated the use of opportunistic selective sampling and entropy.…”