VeReMi Extension: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs
| Autor: | Arnaud Kaiser, Joseph Kamel, Michael Wolf, Frank Kargl, Rens Wouter van der Heijden, Pascal Urien |
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| Přispěvatelé: | IRT SystemX (IRT SystemX), Institute of Distributed Systems, Universität Ulm - Ulm University [Ulm, Allemagne], Télécom ParisTech |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
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Vehicular ad hoc network SIMPLE (military communications protocol) Computer science Intelligent Transport Systems 05 social sciences 050801 communication & media studies computer.software_genre Field (computer science) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Set (abstract data type) Misbehavior Detection [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] 0508 media and communications [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] 11. Sustainability 0502 economics and business Benchmark (computing) [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY] 050211 marketing Data mining Vehicular Networks Intelligent transportation system computer Dataset |
| Zdroj: | ICC 2020-2020 IEEE International Conference on Communications (ICC) IEEE International Conference on Communications (ICC) IEEE International Conference on Communications (ICC), Jun 2020, Dublin (virtual), Ireland. ⟨10.1109/ICC40277.2020.9149132⟩ ICC |
| DOI: | 10.1109/ICC40277.2020.9149132⟩ |
| Popis: | Virtual conference; International audience; Cooperative Intelligent Transport Systems (C-ITS) is a new upcoming technology that aims at increasing road safety and reducing traffic accidents. C-ITS is based on peer-to-peer messages sent on the Vehicular Ad hoc NETwork (VANET). VANET messages are currently authenticated using digital keys from valid certificates. However, the authenticity of a message is not a guarantee of its correctness. Consequently, a misbehavior detection system is needed to ensure the correct use of the system by the certified vehicles. Although a large number of studies are aimed at solving this problem, the results of these studies are still difficult to compare, reproduce and validate. This is due to the lack of a common reference dataset. For this reason, the original VeReMi dataset was created. It is the first public misbehavior detection dataset allowing anyone to reproduce and compare different results. VeReMi is used in a number of studies and is currently the only dataset in its field. In this Paper, we extend the dataset by adding realistic a sensor error model, a new set of attacks and larger number of data points. Finally, we also provide benchmark detection metrics using a set of local detectors and a simple misbehavior detection mechanism. |
| Databáze: | OpenAIRE |
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