{"id":"W3167724116","doi":"10.1109/icjece.2021.3072008","title":"Detecting DNS Typo-Squatting Using Ensemble-Based Feature Selection & Classification Models","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Feature selection; Vulnerability (computing); Squatting position; Feature (linguistics); Domain (mathematical analysis); The Internet; Feature extraction; Data mining; Computer security; Artificial intelligence; World Wide Web; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001554533,0.001171406,0.001188118,0.001843853,0.0004638662,0.001055277,0.0008234713,0.0007033857,0.0006400992],"category_scores_gemma":[0.002681256,0.0001947474,0.001094329,0.001066067,0.0002176336,0.0009162024,0.0007547908,0.001046682,0.0004265269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005396107,"about_ca_system_score_gemma":0.0006197403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006723594,"about_ca_topic_score_gemma":0.004382472,"domain_scores_codex":[0.9992274,0.0001625077,0.00006524059,0.0001840997,0.0002117471,0.0001489235],"domain_scores_gemma":[0.9985378,0.0005798691,0.0001667239,0.0001733697,0.0004708329,0.00007146646],"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":[0.0005686574,0.0006924456,0.05729181,0.00008151905,0.0004356097,0.0004447444,0.0001190475,0.2957468,0.01262098,0.001135284,0.007358164,0.6235049],"study_design_scores_gemma":[0.000003895356,0.00006095396,0.003063895,0.000005851504,0.00004415467,0.00005412405,0.00001917866,0.9943922,0.001615558,0.000332247,0.0003991904,0.000008725137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4338402,0.001543819,0.5572696,0.0006552668,0.0002776338,0.0001355671,0.0008197696,0.002879011,0.002579039],"genre_scores_gemma":[0.9591332,0.0002601062,0.03769688,0.00009604327,0.00007635572,0.00005858725,0.001130196,0.00002627456,0.001522282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006723594,"threshold_uncertainty_score":0.0133689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515380312996899,"score_gpt":0.2082712107595045,"score_spread":0.1831174076295355,"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."}}