{"id":"W2985666906","doi":"10.1609/aaai.v34i04.5724","title":"Midas: Microcluster-Based Detector of Anomalies in Edge Streams","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Anomaly detection; Denial-of-service attack; Computer science; Graph; Enhanced Data Rates for GSM Evolution; Detector; Constant (computer programming); State (computer science); Anomaly (physics); Theoretical computer science; Artificial intelligence; Algorithm; Physics","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":[0.0006122546,0.001009765,0.001037523,0.003479196,0.0003860831,0.0009014003,0.001353773,0.0007907912,0.001145012],"category_scores_gemma":[0.003421705,0.0002662224,0.0004874337,0.001560013,0.0003753776,0.001293232,0.0008352841,0.0008492233,0.0007983244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004611953,"about_ca_system_score_gemma":0.0004158437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001484881,"about_ca_topic_score_gemma":0.00242621,"domain_scores_codex":[0.9995322,0.00007201501,0.00003240033,0.0001398968,0.0001599374,0.00006360195],"domain_scores_gemma":[0.9981346,0.0006763606,0.0003803716,0.0002487757,0.0003982897,0.0001615514],"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":[0.002332648,0.0007632363,0.0815562,0.0004880875,0.000397222,0.001021694,0.0003784262,0.1049073,0.1048218,0.0103483,0.03476218,0.658223],"study_design_scores_gemma":[0.00002415265,0.0001113631,0.004967876,0.000009091843,0.00002366998,0.0003909723,0.00005007978,0.9690396,0.01757787,0.004930421,0.002853312,0.00002165265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.257722,0.001399728,0.7014028,0.0006991577,0.0002828554,0.0003227674,0.003614064,0.03193638,0.00262029],"genre_scores_gemma":[0.7323281,0.0003707499,0.2597432,0.000246911,0.0001330195,0.0001978208,0.003812132,0.0003083116,0.002859627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003479196,"threshold_uncertainty_score":0.003830433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07525842876823527,"score_gpt":0.2812455104167014,"score_spread":0.2059870816484661,"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."}}