{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003023164,0.00162575,0.0004581993,0.001248702,0.0003228785,0.0005609179,0.001095038,0.0008691947,0.001653597],"category_scores_gemma":[0.001393096,0.0004496601,0.0007339001,0.0008272774,0.0004028161,0.001108638,0.0006537187,0.001196673,0.000956258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008097987,"about_ca_system_score_gemma":0.0009139573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01656972,"about_ca_topic_score_gemma":0.02756145,"domain_scores_codex":[0.9997823,0.0000349697,0.00001202874,0.00007079658,0.00006009305,0.00003983569],"domain_scores_gemma":[0.999572,0.0001581725,0.00005258439,0.00006368673,0.0001299799,0.00002355677],"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":[0.000340022,0.0003035957,0.0103504,0.0003286605,0.000250404,0.0004854057,0.0001926072,0.411212,0.01732499,0.004921623,0.04858379,0.5057065],"study_design_scores_gemma":[0.000009424391,0.00004905509,0.0007920807,0.00001007811,0.00001507703,0.0000526794,0.00001654837,0.9905019,0.00483528,0.001848173,0.001859182,0.00001049077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3513584,0.002001357,0.5860775,0.001552391,0.0006684254,0.0002772568,0.008091427,0.04370749,0.006265766],"genre_scores_gemma":[0.7430179,0.0007881001,0.22213,0.0006001734,0.0001220012,0.0002859996,0.020778,0.0009661697,0.01131177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01656972,"threshold_uncertainty_score":0.03294659,"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."}}