{"id":"W1496553157","doi":"10.1109/wimob.2005.1512845","title":"Anomaly-based intrusion detection using mobility profiles of public transportation users","year":2006,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Anomaly (physics); Anomaly detection; Intrusion detection system; Computer science; Intrusion; Public transport; Computer security; Data mining; Geology; Transport engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008681622,0.0003490271,0.0004871784,0.001006962,0.0002180732,0.0006157444,0.0004162905,0.0003932206,0.0001973652],"category_scores_gemma":[0.006546604,0.0001580492,0.0002044638,0.000714874,0.0002400004,0.001170245,0.0003301839,0.0004178476,0.0001197669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000353286,"about_ca_system_score_gemma":0.0003174331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001284,"about_ca_topic_score_gemma":0.0009969387,"domain_scores_codex":[0.9993683,0.0002115721,0.00005448658,0.00009287636,0.0002062693,0.00006664824],"domain_scores_gemma":[0.9966801,0.001642465,0.0006634886,0.0003182111,0.0005584247,0.0001373659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002102875,0.0004689093,0.236608,0.0001626709,0.0002765715,0.0007801457,0.0005650799,0.2338485,0.05487064,0.00567632,0.001467513,0.4631727],"study_design_scores_gemma":[0.00001204834,0.0002457433,0.01558482,0.000009169338,0.00004321133,0.0004516522,0.00008713076,0.9652264,0.01606015,0.001687281,0.00056975,0.00002259947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8453915,0.0001272557,0.1524916,0.0002160311,0.00003207679,0.00005995288,0.0002127593,0.0005787552,0.0008901739],"genre_scores_gemma":[0.9875779,0.00004960882,0.01208397,0.000008166216,0.000009185982,0.00001242715,0.000110232,0.000004687729,0.0001438549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001284,"threshold_uncertainty_score":0.004591286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002894933630237,"score_gpt":0.2288134062057146,"score_spread":0.2087844568694122,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}