{"id":"W3112601954","doi":"10.1109/smc42975.2020.9282846","title":"MA2DF: A Multi-Agent Anomaly Detection Framework","year":2020,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Anomaly detection; Computer science; Workflow; Data mining; Event (particle physics); TRACE (psycholinguistics); Focus (optics); Process (computing); System call; Distributed computing; Key (lock); Business process; Real-time computing; Work in process; Computer security; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008701907,0.00009531146,0.00009254582,0.00003743798,0.0001409828,0.0001245417,0.0003559041,0.00009201317,0.0001565811],"category_scores_gemma":[0.00007449438,0.00008737919,0.00005520985,0.0006654576,0.0000149605,0.0002268629,0.0001799462,0.000212136,0.0005134384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002285778,"about_ca_system_score_gemma":0.00001476803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003058447,"about_ca_topic_score_gemma":0.00001873511,"domain_scores_codex":[0.999127,0.00004948589,0.0001531106,0.0003257399,0.0001661028,0.0001785018],"domain_scores_gemma":[0.9994835,0.00003490466,0.00004499774,0.0002417936,0.00005081388,0.0001440042],"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":[0.00007644374,0.0002664399,0.0005322684,0.00005095619,0.00006220683,0.00005811458,0.005257736,0.004109843,0.02162236,0.08597174,0.003114469,0.8788774],"study_design_scores_gemma":[0.0001748958,0.0001505535,0.001091038,0.000007437214,0.000002867796,0.000009565395,0.00001892156,0.9543932,0.02068706,0.001122635,0.02218337,0.0001584701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02473899,0.00007079801,0.9710348,0.00215629,0.0005504107,0.0001068132,2.340098e-7,0.0004956018,0.0008461212],"genre_scores_gemma":[0.9054118,0.00001732759,0.09120682,0.003045972,0.0002278741,0.000008425508,2.056824e-7,0.000005605538,0.00007599794],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9502833,"threshold_uncertainty_score":0.6599383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827194217418779,"score_gpt":0.2408922576786744,"score_spread":0.2126203155044866,"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."}}