Simulation Framework for Misbehavior Detection in Vehicular Networks

Abstract

Cooperative Intelligent Transport Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Unit (RSU) cooperate by broadcasting V2X messages over the vehicular network. Safety applications use these data to detect and avoid dangerous situations on time. MisBehavior Detection (MBD) in C-ITS is an active research topic which consists of monitoring data semantics of the exchanged Vehicle-to-X communication (V2X) messages to detect and identify potential misbehaving entities. The detection process consists of performing plausibility and consistency checks on the received V2X messages. If an anomaly is detected, the entity may report it by sending a Misbehavior Report (MBR) to the Misbehavior Authority (MA). The MA will then investigate the event and decide to revoke the sender or not. In this paper, we present a MisBehavior Detection (MBD) simulation framework that enables the research community to develop, test, and compare MBD algorithms. We also demonstrate its capabilities by running example scenarios and discuss their results. Framework For Misbehavior Detection (F 2 MD) is open source and available for free on our github.

RTED-SD: A Real-Time Edge Detection Scheme for Sybil DDoS in the Internet of Vehicles

“…In this paper, we use the Framework For Misbehavior Detection (F2MD) [12] as the simulation framework. F2MD is an extension of VEINS [32], which is an open source framework for vehicular network simulations used in OMNET++ a network simulator, together with SUMO, a road traffic simulator.…”

“…False Negative False Negative + True Negative (12) However, in the scenario of most DDoS attacks, we care more about the temporal performance. On the basis as is shown in Fig.…”

“…And we set 3 subsections to explain the concepts of Traffic Processor, Entropy Calculator, and Detection Module in detail. In Section V, we make simulations with open-source framework F2MD [12] and use F2MD datasets to prove and evaluate the detection scheme we propose. In Section VI, we conclude the whole paper and point out the direction of future research.…”

Experimentation and Assessment of Pseudonym Certificate Management and Misbehavior Detection in C-ITS

“…One of the key results of SCA project is the F2MD simulator implemented on VEINS [22], [23] which provides local misbehaviour detection on received CAM and was used to assess multiple, configurable basic plausibility checks on CAM mobility data (such as location, speed, heading …) and to compare detection efficiency and performance of multiple intelligent local detection applications using fixed algorithms and others method based on artificial intelligence [24], [25].…”

Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations

“…The authors of [ 35 ] discuss the security threats in CP and propose possible countermeasures in V2X network protocols, while the work in [ 36 ] focuses on using CP for detecting vehicle misbehaviour due to adversarial attacks in V2X communication. Most of the CP related use cases studied in the literature are safety related, including cooperative driving [ 5 , 16 ], cooperative advisory warnings [ 37 , 38 ], cooperative collision avoidance [ 4 , 13 , 39 ], intersection assistance [ 17 , 40 ], and vehicle misbehaviour detection [ 41 ], to name a few. It is presented in [ 42 ] quantitative comparison of V2V and V2I connectivity on improving sensing redundancy and collaborative sensing coverage for CAV applications.…”