{"id":"W3102902599","doi":"10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00077","title":"Feature Extraction Approach to Unearth Domain Generating Algorithms (DGAs)","year":2020,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Blacklisting; Malware; Domain (mathematical analysis); Feature extraction; Process (computing); Key (lock); Feature (linguistics); Data mining; Domain name; Artificial intelligence; Machine learning; Algorithm; Computer security; The Internet; World Wide Web; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0001659101,0.0001095609,0.000104781,0.00004275901,0.0002101035,0.0002382269,0.0003228624,0.00007979648,0.00002575301],"category_scores_gemma":[0.00001902911,0.00009692076,0.0000482675,0.0006506938,0.00000840445,0.0004608927,0.0001510977,0.000229497,0.0001243055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002001578,"about_ca_system_score_gemma":0.00001837337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009584885,"about_ca_topic_score_gemma":0.000004511095,"domain_scores_codex":[0.9989665,0.00006389904,0.0001135502,0.0004133547,0.0002391592,0.0002035706],"domain_scores_gemma":[0.9994916,0.00001768413,0.00004009715,0.0002161944,0.00004369142,0.0001907361],"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":[0.00004204345,0.000198085,0.00004822973,0.00004179932,0.00004026254,0.00002480451,0.007589887,0.03172499,0.05123914,0.1148512,0.1743027,0.6198969],"study_design_scores_gemma":[0.0001347541,0.000112754,0.00005369666,0.000003534007,0.000001546018,0.00002584099,0.00007021253,0.8253914,0.004786854,0.0002952263,0.1689671,0.000157063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007229845,0.00004320857,0.9775819,0.007808281,0.0003445836,0.000168487,7.02968e-7,0.0003240677,0.006498906],"genre_scores_gemma":[0.07611302,0.000007838819,0.9152692,0.007060316,0.0009837283,0.00001689962,0.000004962197,0.000009529705,0.0005345225],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7936664,"threshold_uncertainty_score":0.3952314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211063521482082,"score_gpt":0.2373094046221912,"score_spread":0.216203052473983,"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."}}