{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000850061,0.001146471,0.0007956006,0.00163354,0.0005048024,0.001098438,0.001530333,0.001432402,0.002575656],"category_scores_gemma":[0.001427022,0.0005096272,0.001059184,0.001041965,0.0006117714,0.001625404,0.001244849,0.002231154,0.001343283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162582,"about_ca_system_score_gemma":0.001303546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009598512,"about_ca_topic_score_gemma":0.0103519,"domain_scores_codex":[0.9994831,0.00009063136,0.00003100343,0.0001223206,0.0001864299,0.00008646488],"domain_scores_gemma":[0.9995926,0.0001135257,0.00003577106,0.00005524998,0.0001710712,0.00003176809],"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":[0.0001503263,0.0002467599,0.002355503,0.0002147214,0.0001823391,0.0001766809,0.00009083555,0.2836353,0.009499601,0.04539331,0.01795345,0.6401011],"study_design_scores_gemma":[0.000005493644,0.00002428551,0.0001888244,0.00001676076,0.00001156768,0.00003478094,0.000007195472,0.9803194,0.001594733,0.01377472,0.004012987,0.000009213283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004946337,0.00129222,0.9886875,0.0005808769,0.0001196214,0.00006618959,0.0003187215,0.001819415,0.002169115],"genre_scores_gemma":[0.3201381,0.00267587,0.6580096,0.001062655,0.0003171285,0.0003866271,0.002502255,0.0002621749,0.01464554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009598512,"threshold_uncertainty_score":0.01908529,"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."}}