{"id":"W2150797223","doi":"10.1109/ares.2007.45","title":"AsmLSec: An Extension of Abstract State Machine Language for Attack Scenario Specification","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Programming language; Specification language; Compiler; Software security assurance; Formal specification; Syntax; Formal methods; Finite-state machine; Software engineering; Computer security; Security service; Information security; Artificial intelligence","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":[0.0005270175,0.00009104394,0.0001094408,0.0001234664,0.00004913082,0.00002489599,0.000320169,0.00003997166,0.00002501428],"category_scores_gemma":[0.00004006233,0.00008132287,0.00004095771,0.0001639195,0.00002411237,0.000540612,0.000052017,0.00007139134,0.000008911791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003941189,"about_ca_system_score_gemma":0.00001435179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006972569,"about_ca_topic_score_gemma":0.0001451132,"domain_scores_codex":[0.9990938,0.000009324651,0.0002756525,0.0002887503,0.0001579918,0.0001744539],"domain_scores_gemma":[0.9989934,0.00007643363,0.0001506293,0.0005541953,0.0001619339,0.00006340592],"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":[0.00006794061,0.000151974,0.0000889494,0.00002814776,0.000005247566,0.000007977142,0.0006415483,0.0001621023,0.305068,0.004969976,0.0003860903,0.688422],"study_design_scores_gemma":[0.000242885,0.0002780571,0.0156779,0.00001138907,0.000001940786,0.00001180478,0.00006713661,0.0111356,0.9660471,0.002785231,0.003574294,0.0001666442],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07735167,0.00003316724,0.9208598,0.00007410841,0.00009027131,0.0002696223,0.000004776244,0.000434376,0.000882184],"genre_scores_gemma":[0.6319641,0.000006791374,0.3675246,0.0000642847,0.00002329064,0.000004594061,0.000006548178,0.000008051881,0.0003977929],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6882554,"threshold_uncertainty_score":0.331625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03290355953923618,"score_gpt":0.3410298587223086,"score_spread":0.3081262991830724,"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."}}