{"id":"W4360764539","doi":"10.1109/icmla55696.2022.00173","title":"VDGraph2Vec: Vulnerability Detection in Assembly Code using Message Passing Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canada Research Chairs","keywords":"Computer science; Vulnerability (computing); Malware; Deep learning; Reverse engineering; Software; Artificial intelligence; Benchmark (surveying); Task (project management); Machine learning; Process (computing); Vulnerability management; Artificial neural network; Software security assurance; Code (set theory); Software engineering; Static program analysis; Computer security; Vulnerability assessment; Software development; Information security; Programming language; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001254524,0.0001129344,0.000132104,0.0002568884,0.000304274,0.0001666328,0.0006408471,0.00003889221,0.00002866493],"category_scores_gemma":[0.0002025013,0.0001204891,0.00005318935,0.001480881,0.00002339101,0.0004158695,0.0006575739,0.0006096089,8.816151e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003337757,"about_ca_system_score_gemma":0.00004071651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003133346,"about_ca_topic_score_gemma":0.0001065532,"domain_scores_codex":[0.9981993,0.000302332,0.0002108186,0.000421559,0.000437221,0.000428785],"domain_scores_gemma":[0.9988264,0.0005330442,0.00003576306,0.0004950413,0.00003507087,0.00007465169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006985869,0.00005208497,0.03301702,0.000007567309,0.000005117678,0.00003665764,0.0001282645,0.9187102,0.003160404,0.0002100502,0.00002582748,0.04463984],"study_design_scores_gemma":[0.0001591375,0.0000396288,0.04476165,0.000002603886,9.786033e-7,0.00002723922,0.00002048419,0.9537589,0.0009267083,0.0001092736,0.00006421408,0.0001291792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4622554,0.00003637414,0.5369592,0.00006563723,0.0003486111,0.00008811527,3.20429e-7,0.0002162546,0.00003007537],"genre_scores_gemma":[0.9932211,7.217827e-7,0.006608534,0.00005986636,0.00003718827,0.00003516049,6.688071e-7,0.00001283517,0.00002391244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5309657,"threshold_uncertainty_score":0.4913404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03022265963227662,"score_gpt":0.2901507343406545,"score_spread":0.2599280747083779,"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."}}