{"id":"W2237959143","doi":"10.1016/j.eswa.2016.01.002","title":"Malicious sequential pattern mining for automatic malware detection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Malware; Computer science; Executable; Data mining; Classifier (UML); Trojan; Cryptovirology; Intrusion detection system; Sequential Pattern Mining; System call; Artificial intelligence; Machine learning; Computer security; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001596572,0.0001369009,0.0001457189,0.00009491017,0.0003536149,0.0001326323,0.0003654247,0.0000795201,0.00001116458],"category_scores_gemma":[0.000008112473,0.00009188359,0.0000493679,0.0002600625,0.00003177159,0.0003384569,0.00005245539,0.00004060191,0.00006041205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000971686,"about_ca_system_score_gemma":0.00003262573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008453031,"about_ca_topic_score_gemma":0.00005479564,"domain_scores_codex":[0.99885,0.00005059435,0.000266849,0.000393357,0.0001937382,0.0002454862],"domain_scores_gemma":[0.9989607,0.0001137397,0.0001610126,0.0005536542,0.0001244955,0.00008641611],"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":[0.0000166903,0.00009191655,0.0001562009,0.00009980542,0.00006310052,0.0000023132,0.001474932,0.0001018892,0.02485085,0.008133386,0.002707761,0.9623011],"study_design_scores_gemma":[0.002569567,0.0008303615,0.000484748,0.0006504443,0.00003452958,0.0005246341,0.0004910989,0.50497,0.03360084,0.0009515826,0.4537331,0.001159124],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006423121,0.0001662352,0.9906976,0.0005634602,0.0003900392,0.001145842,0.000007330494,0.0004727258,0.0001336197],"genre_scores_gemma":[0.9861133,0.00001123023,0.007889276,0.000123495,0.0005172725,0.00504606,0.000003199318,0.00001915018,0.0002770802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9828084,"threshold_uncertainty_score":0.3746904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557217367219011,"score_gpt":0.2475662252126508,"score_spread":0.2319940515404607,"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."}}