{"url":"/dataset/cic-ddos2019","name":"CIC-DDoS2019","full_name":"CIC-DDoS2019","description_markdown":"his is an academic intrusion detection dataset.\r\nAll the credit goes to the original authors: Dr. Iman Sharafaldin, Dr. Saqib Hakak, Dr. Arash Habibi Lashkari Dr. Ali Ghorbani. Please cite their original paper.\r\n\r\nThe dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/dhoogla/cicddos2019","introduced_date":"2020-02-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/lucid-a-practical-lightweight-deep-learning","title":"LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["CIC-DDoS2019"],"data_loaders":[],"num_papers_in_archive":9,"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."}