{"url":"/dataset/ff-ann-id-intrusion-detection-in-wsns","name":"FF-ANN-ID: Intrusion detection in WSNs","full_name":null,"description_markdown":"This dataset consists of six columns. The first four columns represent the input features (i.e., area, sensing range, transmission range, and the number of sensors). The last two columns represent the response variable or target variable (i.e., number of barriers (Gaussian) and number of barriers (Uniform)).","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/abhilashdata/ffannid-intrusion-detection-in-wsns","introduced_date":"2022-08-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-deep-learning-approach-to-predict-the","title":"A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks","first_author":"Abhilash Singh","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["FF-ANN-ID: Intrusion detection in WSNs"],"data_loaders":[],"num_papers_in_archive":1,"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":"2026-09-25T09:33:49+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."}