{"id":"W4312856608","doi":"10.1109/icbaie56435.2022.9985850","title":"Microsoft Malware Prediction Using LightGBM Model","year":2022,"lang":"en","type":"article","venue":"2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Malware; Computer science; Artificial intelligence; Machine learning; Metric (unit); Heuristic; Feature (linguistics); Feature engineering; Data mining; Deep learning; Computer security; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0005034609,0.0002759663,0.0002611565,0.0005066275,0.0001519604,0.0002208482,0.002440789,0.00007934892,0.0001477588],"category_scores_gemma":[0.0001682415,0.0003119469,0.000074271,0.0003218171,0.000074141,0.001290729,0.002104555,0.0005808883,0.000007985058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001960541,"about_ca_system_score_gemma":0.00009444456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001664485,"about_ca_topic_score_gemma":0.000006088324,"domain_scores_codex":[0.9975413,0.00004129083,0.0006714753,0.000790279,0.0006692562,0.0002864055],"domain_scores_gemma":[0.9985465,0.00007145452,0.0002929107,0.0007487444,0.0002452604,0.00009508336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001787394,0.0003137126,0.00005956264,0.00007097496,0.0001522274,0.00002939965,0.0023419,0.1351635,0.1500972,0.5429704,0.0009344257,0.1676879],"study_design_scores_gemma":[0.00003496912,0.0002175684,0.000005891386,0.0000788632,0.000008747299,0.00004167394,0.0001415344,0.8997027,0.08680707,0.01091156,0.001803906,0.0002454697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01134871,0.00006329073,0.98506,0.0004491125,0.001929567,0.000200365,0.0002232263,0.0003075693,0.0004182055],"genre_scores_gemma":[0.9609392,0.0001074698,0.03818337,0.0002310329,0.0001271115,0.00004122504,0.0001204719,0.00002749488,0.0002226104],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9495905,"threshold_uncertainty_score":0.9999332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1502154506320312,"score_gpt":0.3035154548151475,"score_spread":0.1533000041831163,"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."}}