{"id":"W2040146912","doi":"10.1109/infcomw.2014.6849283","title":"Behavioral analytics for inferring large-scale orchestrated probing events","year":2014,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Exploit; Malware; Context (archaeology); Computer security; Event (particle physics); The Internet; Data science; Botnet; Cyberspace; Analytics; Task (project management); World Wide Web","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":[0.0003557095,0.00008054183,0.00009571343,0.00006088956,0.0001640012,0.00008295034,0.0002838289,0.00006230504,0.00001778577],"category_scores_gemma":[0.00001399984,0.00007403119,0.00005326423,0.0002491461,0.00000793221,0.0003571868,0.0001084623,0.00009043917,0.00001617623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002452152,"about_ca_system_score_gemma":0.00001937589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001664762,"about_ca_topic_score_gemma":0.000107125,"domain_scores_codex":[0.9992194,0.00003103862,0.0001635859,0.0002221524,0.0001195106,0.0002443521],"domain_scores_gemma":[0.9995439,0.00003199863,0.00005029825,0.0002375924,0.00007170475,0.00006451605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000985687,0.002193023,0.01955392,0.0002154899,0.0001027587,0.000006178879,0.009613388,0.01489171,0.01620645,0.4692804,0.007887576,0.4599505],"study_design_scores_gemma":[0.0003884731,0.000211668,0.0008605391,0.00001746615,0.000007585532,0.000003459438,0.00002124475,0.9755375,0.005548135,0.005731217,0.01152212,0.0001505707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1740233,0.000002942244,0.8245084,0.0001144601,0.0002504474,0.0001433726,5.464383e-7,0.0001650688,0.0007915727],"genre_scores_gemma":[0.9590929,0.000001554536,0.04030221,0.0001436894,0.00009516183,0.0000167767,0.00000318308,0.000005887729,0.0003386871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9606458,"threshold_uncertainty_score":0.3018904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03070908730024404,"score_gpt":0.2812500738414376,"score_spread":0.2505409865411936,"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."}}