{"id":"W2950977322","doi":"10.48550/arxiv.1905.07573","title":"The Curious Case of Machine Learning In Malware Detection","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; University of Windsor","funders":"","keywords":"Malware; Computer science; Machine learning; Artificial intelligence; Malware analysis; Cryptovirology; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01789398,0.001796342,0.002267891,0.004444688,0.003325706,0.009220768,0.003086212,0.009845062,0.003704077],"category_scores_gemma":[0.06151363,0.001116739,0.001249036,0.00282259,0.02022858,0.03292121,0.0055536,0.01638836,0.002427498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002626163,"about_ca_system_score_gemma":0.001625998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001397558,"about_ca_topic_score_gemma":0.0007461278,"domain_scores_codex":[0.9815896,0.009053422,0.0005331826,0.003102847,0.005172125,0.00054875],"domain_scores_gemma":[0.9148896,0.07399685,0.00151626,0.005214121,0.003664221,0.0007189008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005595214,0.00005871894,0.001375641,0.0004767703,0.00007582608,0.0001995367,0.000476813,0.004979618,0.0003663265,0.8922764,0.01790466,0.08175372],"study_design_scores_gemma":[0.00001399126,0.00003123195,0.0002376717,0.0002949551,0.00001236123,0.0002697545,0.0001104173,0.02490605,0.0006369663,0.9419541,0.03149042,0.00004212301],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008353829,0.07192714,0.6795866,0.1814327,0.004156684,0.0001281351,0.00023739,0.0007437979,0.05343376],"genre_scores_gemma":[0.5052285,0.05883527,0.3433149,0.04488665,0.0245961,0.0007269047,0.0003589047,0.0007104874,0.02134227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01789398,"threshold_uncertainty_score":0.09463346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03144275419192749,"score_gpt":0.1928912084604873,"score_spread":0.1614484542685598,"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."}}