{"id":"W3117510159","doi":"10.1109/icm50269.2020.9331794","title":"Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Botnet; Computer science; Internet of Things; Intrusion detection system; Malware; Machine learning; Robustness (evolution); Artificial intelligence; Software deployment; Decision tree; Computer security; Computer network; The Internet; Data mining","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007631405,0.0003831341,0.0004870307,0.0002104019,0.0001930117,0.000360772,0.001348528,0.0004018087,0.0001153626],"category_scores_gemma":[0.00007187566,0.0003876139,0.0001347545,0.0003458556,0.00003707034,0.0002999657,0.002880316,0.00246221,0.00004336634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001489644,"about_ca_system_score_gemma":0.00004260396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000382101,"about_ca_topic_score_gemma":0.00004725608,"domain_scores_codex":[0.9967918,0.0005359544,0.0005448317,0.001295763,0.0004134241,0.0004182339],"domain_scores_gemma":[0.998695,0.00006973267,0.0002775188,0.0007554764,0.00001313122,0.0001891959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006726348,0.0002190957,0.0002655202,0.00007894532,0.00004210237,0.00002882896,0.002794773,0.9125524,0.0007062951,0.0007493435,0.00005145126,0.08244403],"study_design_scores_gemma":[0.0005326732,0.000136808,0.0001481341,0.00003885475,0.00000754921,0.00001250576,0.00004124048,0.9939204,0.0006762039,0.0009821772,0.003077767,0.0004256431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02108295,0.0002473338,0.972008,0.0003146347,0.000523906,0.000469866,0.000001612665,0.0004576691,0.004894052],"genre_scores_gemma":[0.6886965,0.0001938783,0.3099195,0.0004312494,0.0002891055,0.00007651111,0.00004979495,0.00003934489,0.0003041137],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6676136,"threshold_uncertainty_score":0.9998576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03085016425356251,"score_gpt":0.2506407947765111,"score_spread":0.2197906305229486,"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."}}