{"id":"W2962590879","doi":"10.1109/tnsm.2020.2972405","title":"BotChase: Graph-Based Bot Detection Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Royal Bank of Canada","keywords":"Computer science; Robustness (evolution); Leverage (statistics); Graph; Machine learning; Network topology; Artificial intelligence; Overhead (engineering); Data mining; Distributed computing; Theoretical computer science; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000769719,0.001407182,0.001078849,0.003413867,0.0005966997,0.0008819544,0.002017417,0.001207961,0.001671254],"category_scores_gemma":[0.002900993,0.0005889612,0.0008257858,0.001092286,0.0008015045,0.002072572,0.001753078,0.00141113,0.001153407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007380258,"about_ca_system_score_gemma":0.0009737348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003815451,"about_ca_topic_score_gemma":0.00511828,"domain_scores_codex":[0.9992313,0.0001348172,0.00003863726,0.0002312325,0.0002857729,0.00007830298],"domain_scores_gemma":[0.9981366,0.0006827878,0.0003521191,0.000432109,0.0002639073,0.0001324346],"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":[0.0006885799,0.001188168,0.01888825,0.0006994029,0.0006094223,0.0006067183,0.0003151862,0.2006259,0.05808643,0.009911841,0.04504424,0.6633359],"study_design_scores_gemma":[0.00002352265,0.00007037423,0.001360919,0.000008759595,0.00001144877,0.00009898901,0.00001542809,0.9841146,0.007776735,0.004483606,0.002011651,0.00002401747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04643273,0.0003625412,0.8384205,0.0003148799,0.0001176549,0.000427854,0.001232482,0.1106146,0.002076822],"genre_scores_gemma":[0.5003634,0.0002672104,0.4893589,0.0004123341,0.00008305701,0.0003518115,0.003791732,0.001666085,0.003705483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003815451,"threshold_uncertainty_score":0.007586479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200293515757753,"score_gpt":0.2149806893712793,"score_spread":0.194951337795504,"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."}}