{"url":"/dataset/veremi","name":"VeReMi","full_name":null,"description_markdown":"The Vehicular Reference Misbehavior (VeReMi) dataset, is a dataset for the evaluation of misbehavior detection mechanisms for VANETs (vehicular networks). This dataset consists of message logs of on-board units, including a labelled ground truth, generated from a simulation environment. The dataset includes malicious messages intended to trigger incorrect application behavior, which is what misbehavior detection mechanisms aim to prevent. The initial dataset contains a number of simple attacks: the idea of this dataset release is not just to provide a baseline for the comparison of detection mechanisms, but also to serve as a starting point for more complex attacks.","description_withheld":null,"homepage":"https://veremi-dataset.github.io/","introduced_date":"2018-04-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/veremi-a-dataset-for-comparable-evaluation-of","title":"VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["VeReMi"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}