{"id":"W2998647325","doi":"10.1609/aaai.v34i04.5724","title":"Midas: Microcluster-Based Detector of Anomalies in Edge Streams","year":2020,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Denial-of-service attack; Anomaly detection; Computer science; Graph; Enhanced Data Rates for GSM Evolution; Detector; Constant (computer programming); State (computer science); Theoretical computer science; Artificial intelligence; Algorithm; Programming language; Operating system; The Internet","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007337732,0.00007653596,0.0001247846,0.00006688678,0.00002496095,0.00003156738,0.0003612073,0.00004790763,0.00009780322],"category_scores_gemma":[0.00001964767,0.00006934488,0.00004466961,0.0004613,0.00002638886,0.0001902568,0.000111985,0.00008188602,0.00001930526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001376063,"about_ca_system_score_gemma":0.00003867823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005948712,"about_ca_topic_score_gemma":0.0001473141,"domain_scores_codex":[0.9993042,0.00004342383,0.0001998729,0.0002091801,0.0001109675,0.0001323678],"domain_scores_gemma":[0.9996285,0.00004964405,0.00005337605,0.0001803024,0.00003054756,0.00005760775],"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":[0.0003376466,0.0006516477,0.01246824,0.0004432824,0.00004157049,0.00006419825,0.007454106,0.005244047,0.2511262,0.01411057,0.006794406,0.7012641],"study_design_scores_gemma":[0.0006501049,0.000409194,0.001583698,0.00003409457,0.000002144132,0.000001992297,0.00005403718,0.5521101,0.439556,0.0002956128,0.005130844,0.0001721949],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7350307,0.000120193,0.2609521,0.001944721,0.0001678473,0.0001355146,0.000001629884,0.0001301062,0.001517315],"genre_scores_gemma":[0.9870146,0.000004704244,0.01192766,0.0009771577,0.00004446221,0.000003550895,5.642593e-7,0.000003506784,0.00002381805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7010919,"threshold_uncertainty_score":0.2827802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661749099101099,"score_gpt":0.2143803362935629,"score_spread":0.1977628453025519,"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."}}