Simulation Framework for Misbehavior Detection in Vehicular Networks

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 Road–Side 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 Cooperative Intelligent Transport Systems (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 (F2MD) is open source and available for free on our github.

[1]  William Whyte,et al.  Towards a Balance between Privacy and Safety: Microsimulation Framework for Assessing Silence-Based Pseudonym-Change Schemes , 2019 .

[2]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[3]  Sorin A. Huss,et al.  A Novel Framework for Efficient Mobility Data Verification in Vehicular Ad-hoc Networks , 2012, Int. J. Intell. Transp. Syst. Res..

[4]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[5]  Frank Kargl,et al.  VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs , 2018, SecureComm.

[6]  Prinkle Sharma,et al.  Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[7]  Pascal Urien,et al.  Misbehavior Reporting Protocol for C-ITS , 2018, 2018 IEEE Vehicular Networking Conference (VNC).

[8]  Travis E. Oliphant,et al.  Guide to NumPy , 2015 .

[9]  William Whyte,et al.  A Security Credential Management System for V2X Communications , 2018, IEEE Transactions on Intelligent Transportation Systems.

[10]  Pascal Urien,et al.  Feasibility Study of Misbehavior Detection Mechanisms in Cooperative Intelligent Transport Systems (C-ITS) , 2018, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).

[11]  Tim Leinmüller,et al.  Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems , 2016, IEEE Communications Surveys & Tutorials.

[12]  Thomas Engel,et al.  Luxembourg SUMO Traffic (LuST) Scenario: 24 hours of mobility for vehicular networking research , 2015, 2015 IEEE Vehicular Networking Conference (VNC).

[13]  Pascal Urien,et al.  CaTch: A Confidence Range Tolerant Misbehavior Detection Approach , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[14]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[15]  Ryan M. Gerdes,et al.  A data trust framework for VANETs enabling false data detection and secure vehicle tracking , 2017, 2017 IEEE Conference on Communications and Network Security (CNS).

[16]  Frank Kargl,et al.  Pseudonym Schemes in Vehicular Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[17]  Xin Zeng,et al.  Misbehavior Detection Based on Support Vector Machine and Dempster-Shafer Theory of Evidence in VANETs , 2018, IEEE Access.

[18]  Maxim Raya,et al.  TraCI: an interface for coupling road traffic and network simulators , 2008, CNS '08.