{"id":"W4400458795","doi":"10.37394/232018.2024.12.33","title":"Enterprise Malware Detection using Digital Forensic Artifacts and Machine Learning","year":2024,"lang":"en","type":"article","venue":"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Malware; Computer science; Digital forensics; Artificial intelligence; Machine learning; Digital evidence; 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":[0.0006756268,0.0005231652,0.0003625954,0.003316717,0.000382105,0.0008620527,0.0004642805,0.0004863238,0.0005456161],"category_scores_gemma":[0.002028085,0.0001681697,0.0004835369,0.0009155409,0.0004865092,0.0008839403,0.0006116238,0.0003451762,0.0002535902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004452057,"about_ca_system_score_gemma":0.0004619314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001151328,"about_ca_topic_score_gemma":0.001632241,"domain_scores_codex":[0.9995003,0.0001110829,0.00002617457,0.00009543756,0.0002067411,0.00006024915],"domain_scores_gemma":[0.9992056,0.0002600203,0.0001811309,0.0001501946,0.0001706191,0.00003236553],"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":[0.0004349965,0.000450634,0.0340998,0.0002518643,0.0001112111,0.0007304734,0.0003363607,0.110155,0.05791109,0.004889195,0.001241249,0.7893882],"study_design_scores_gemma":[0.00001238788,0.0002504401,0.01475525,0.00004933155,0.00003283521,0.000630473,0.0001691428,0.9199489,0.05858577,0.003263989,0.002265391,0.00003611012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5864958,0.0003525215,0.4058663,0.000237058,0.00006482444,0.0002052259,0.0002506935,0.002247311,0.004280252],"genre_scores_gemma":[0.8727114,0.0001552857,0.1260481,0.00002505924,0.00001456281,0.00004385698,0.0001986015,0.00002594696,0.000777205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003316717,"threshold_uncertainty_score":0.00357312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06131836575556536,"score_gpt":0.3384193576143664,"score_spread":0.277100991858801,"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."}}