{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005105946,0.0007256928,0.0005630884,0.002478766,0.0003708592,0.0007666774,0.0007418829,0.0006535421,0.0005812515],"category_scores_gemma":[0.001362232,0.0002388127,0.0004543255,0.001086264,0.000396237,0.0007551238,0.0006604657,0.0005586274,0.0005462895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000666159,"about_ca_system_score_gemma":0.000497597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006522366,"about_ca_topic_score_gemma":0.005129811,"domain_scores_codex":[0.9994426,0.0000885478,0.00004704335,0.000152709,0.0001370224,0.0001319936],"domain_scores_gemma":[0.9991643,0.0002082574,0.0001897918,0.0001192697,0.0002567942,0.00006155736],"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":[0.0005815214,0.0008418259,0.0548476,0.0001400559,0.0001411722,0.000554517,0.0003658447,0.1546969,0.03773149,0.002718741,0.01037119,0.7370091],"study_design_scores_gemma":[0.00000277172,0.00002273518,0.002573104,0.000004683725,0.0000094062,0.00003612573,0.00003226954,0.9928229,0.003828709,0.000369587,0.0002920679,0.000005571098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.723938,0.0004642114,0.2682725,0.0003125361,0.0000689787,0.0001073293,0.0004603005,0.003675371,0.002700811],"genre_scores_gemma":[0.9584144,0.00009406982,0.03849494,0.00008348221,0.00002839469,0.00002904486,0.0008374786,0.00003918766,0.001978978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006522366,"threshold_uncertainty_score":0.01296878,"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."}}