Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks

Abstract

In vehicle-to-everything (V2X) networks, security and safety are inherently difficult tasks due to the distinct characteristics of such networks, such as their highly dynamic topology and frequent connectivity disruptions. Jellyfish attacks are a sort of denial of service attack that are challenging to deal with, since they conform to protocol norms while impairing network performance, particularly in terms of communication overhead and reliability. Numerous existing approaches have developed new techniques with which to identify and prevent these attacks; however, no approach has been capable of facing all three types of Jellyfish attacks, which include reordering attacks, delay variance attacks, and periodic drop attacks. In this work, we design a new protocol that analyzes the behavior of every node in a network and selects the trusted routes for data transmission to their intended destination by calculating different Honesty metrics. The OMNET++ simulator was used to evaluate the overall performance of the proposed protocol. Various evaluation metrics, such as the packet delivery ratio, end-to-end delay, and throughput, are considered and compared with other existing approaches.