{"id":"W2982055583","doi":"10.4018/ijdcf.2020010105","title":"A Deep Learning Framework for Malware Classification","year":2019,"lang":"en","type":"article","venue":"International Journal of Digital Crime and Forensics","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Zayed University; University of Waterloo; Damascus University; Concordia University; York University; Harbin Institute of Technology; University of Alberta; Amrita Vishwa Vidyapeetham University; Institut national de recherche en informatique et en automatique (INRIA); University of Memphis; McGill University; Simon Fraser University; University of Manitoba; University of Ontario Institute of Technology","keywords":"Malware; Computer science; Artificial intelligence; Convolutional neural network; Machine learning; Deep learning; Support vector machine; Computer security","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":[0.00009443281,0.00007377884,0.0001063189,0.0001223291,0.00002814605,0.000274306,0.000353741,0.0000519157,0.000003373501],"category_scores_gemma":[0.0002382096,0.00006649682,0.00007967687,0.00006449253,0.00002468889,0.001269283,0.00008209564,0.0001655347,0.000005687508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000412042,"about_ca_system_score_gemma":0.0000225943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.23589e-7,"about_ca_topic_score_gemma":1.288553e-7,"domain_scores_codex":[0.9992912,0.000006716427,0.0002428941,0.0001198273,0.0002502679,0.00008904328],"domain_scores_gemma":[0.998797,0.0001657382,0.0002810496,0.00009085453,0.0006187893,0.00004656595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008313265,0.00003069517,0.002787155,0.00001163676,0.00006902171,0.000009648551,0.0003124906,0.0001410976,0.0004432286,0.4227307,0.0001357114,0.5732455],"study_design_scores_gemma":[0.0006364285,0.0008808978,0.003699534,0.0001312472,0.00001185219,0.0004576862,0.0002209963,0.01942636,0.006955209,0.9453676,0.02196783,0.0002443727],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009713513,0.00009003207,0.9882118,0.0004001351,0.0006020201,0.00007483074,0.000002506108,0.00004632948,0.0008588371],"genre_scores_gemma":[0.8390186,0.00002592977,0.1606094,0.000104702,0.0001301512,0.000002017745,0.000002609134,0.000007034634,0.00009952022],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8293051,"threshold_uncertainty_score":0.2711662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01844777074460633,"score_gpt":0.2936133347754012,"score_spread":0.2751655640307949,"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."}}