{"id":"W3168412632","doi":"10.1109/syscon48628.2021.9447094","title":"Malware System Calls Detection Using Hybrid System","year":2021,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Anomaly detection; System call; Intrusion detection system; Anomaly-based intrusion detection system; Malware; Data mining; Anomaly (physics); Process (computing); Machine learning; Artificial intelligence; 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":[0.0002591859,0.0006431512,0.0004696437,0.001444353,0.000253209,0.0005458182,0.0004946101,0.0004694016,0.001196589],"category_scores_gemma":[0.000742981,0.0001688784,0.0003893247,0.0004485058,0.0001690217,0.0007289693,0.0005451535,0.0003872463,0.0005066668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004586311,"about_ca_system_score_gemma":0.0003439776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003121906,"about_ca_topic_score_gemma":0.004731795,"domain_scores_codex":[0.9996587,0.0000395478,0.00001679219,0.0001035122,0.000120464,0.00006095947],"domain_scores_gemma":[0.9995809,0.0001145226,0.00005643297,0.00004718237,0.0001681519,0.0000327736],"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":[0.0008524595,0.0006066043,0.02939557,0.0002185235,0.0002928461,0.0006505137,0.0002194806,0.09415564,0.1119056,0.00109453,0.004967446,0.7556408],"study_design_scores_gemma":[0.000007727713,0.0001912376,0.007943187,0.000006759407,0.00003803739,0.000200793,0.00002984174,0.9739226,0.01654008,0.0004650012,0.0006311021,0.00002351289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7388573,0.0007956215,0.2427949,0.0003294768,0.0001609522,0.0001190643,0.0005621446,0.0105271,0.005853437],"genre_scores_gemma":[0.9742033,0.00008588479,0.02335273,0.00008764196,0.00002613988,0.00003495888,0.0003272543,0.00003407313,0.00184792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003121906,"threshold_uncertainty_score":0.006207466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468409148784555,"score_gpt":0.2130639241376363,"score_spread":0.1983798326497908,"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."}}