{"id":"W2142515469","doi":"10.1007/s11416-013-0183-6","title":"Detecting machine-morphed malware variants via engine attribution","year":2013,"lang":"en","type":"article","venue":"Journal of Computer Virology and Hacking Techniques","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Malware; Computer science; Heuristics; Morphing; Set (abstract data type); Attribution; Cryptovirology; Face (sociological concept); Artificial intelligence; Machine learning; Computer security; Programming language; 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":[0.0007024343,0.0002258881,0.0003988547,0.0004384244,0.0001949955,0.0001373309,0.0006475032,0.0002191062,0.00001793098],"category_scores_gemma":[0.00004172039,0.000199309,0.0001049226,0.0002478406,0.00008191289,0.001281557,0.0003886023,0.0005963258,0.000005113245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006062563,"about_ca_system_score_gemma":0.00002633794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001977886,"about_ca_topic_score_gemma":0.000001995441,"domain_scores_codex":[0.9984185,0.0001761797,0.0006096056,0.0002874045,0.0001975698,0.0003107894],"domain_scores_gemma":[0.9984269,0.0001584419,0.0005989693,0.0003319,0.000369476,0.0001143071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002455507,0.00006129251,0.0009358068,0.00002484429,0.00005473552,0.00008074168,0.0001589554,0.0000413559,0.03409681,0.002522778,0.0004181388,0.96158],"study_design_scores_gemma":[0.00145532,0.005203433,0.05545909,0.0004371483,0.00007920426,0.01398945,0.00001208026,0.2068259,0.486059,0.2182721,0.01087574,0.001331515],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03044961,0.0002126972,0.9672734,0.0008873014,0.000308726,0.0002073523,7.34627e-7,0.0006290532,0.00003108972],"genre_scores_gemma":[0.5228622,0.0000570122,0.4764719,0.0004161125,0.0001681867,0.000009484527,4.191905e-7,0.00001020238,0.000004504555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9602485,"threshold_uncertainty_score":0.8127585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007616468304329786,"score_gpt":0.2328550062554915,"score_spread":0.2252385379511617,"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."}}