{"id":"W3106657271","doi":"10.1111/coin.12422","title":"Classifying and clustering malicious advertisement uniform resource locators using deep learning","year":2020,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency; Allianz Industrie Forschung","keywords":"Computer science; Cluster analysis; Latency (audio); Autoencoder; Deep learning; Artificial intelligence; Preprocessor; Artificial neural network; Data mining; Dimensionality reduction; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001689038,0.0001292999,0.0001145159,0.000079166,0.000295289,0.000209137,0.0003256039,0.00004597038,0.000009103214],"category_scores_gemma":[0.00007320571,0.0001407033,0.00003387042,0.0003913256,0.000047415,0.0003515729,0.0003036107,0.0002145076,0.00002114602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007175322,"about_ca_system_score_gemma":0.00003379222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002525755,"about_ca_topic_score_gemma":0.000004442885,"domain_scores_codex":[0.9988568,0.0000586616,0.0002498472,0.0003694857,0.0002702407,0.0001949466],"domain_scores_gemma":[0.9994419,0.0001433977,0.0001005979,0.0001005038,0.00007194139,0.0001416991],"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":[0.000006901549,0.000006003662,0.0006503213,0.00002490072,0.000009422914,0.000008035612,0.002137731,0.9069297,0.0001476972,0.003349762,0.000009407774,0.08672015],"study_design_scores_gemma":[0.00005511512,0.00008071162,0.0006266635,0.00003641501,0.000004833503,0.00003841591,0.0002412919,0.9950698,0.0006322835,0.001947864,0.001106408,0.0001602088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02692454,0.0002550965,0.9715706,0.0006120529,0.0001318529,0.00008696505,2.907439e-7,0.0001736563,0.0002449669],"genre_scores_gemma":[0.9280061,0.00001191806,0.07113996,0.0007303978,0.00008704341,0.000002541444,0.000002605508,0.00001015915,0.000009324982],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9010815,"threshold_uncertainty_score":0.5737714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05010105212257533,"score_gpt":0.2715572674456522,"score_spread":0.2214562153230769,"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."}}