{"id":"W4400853529","doi":"10.1145/3669901","title":"VeriBin: A Malware Authorship Verification Approach for APT Tracking through Explainable and Functionality-Debiasing Adversarial Representation Learning","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Privacy and Security","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Debiasing; Adversarial system; Computer science; Representation (politics); Malware; Artificial intelligence; Tracking (education); Machine learning; Computer security; Psychology; Cognitive science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001602707,0.001386377,0.000766269,0.001446974,0.0005467563,0.001221596,0.00247129,0.001898675,0.004073974],"category_scores_gemma":[0.00667807,0.0004572797,0.001108275,0.0007668622,0.001051656,0.002886738,0.002222086,0.002588182,0.001761531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001540103,"about_ca_system_score_gemma":0.001082429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003236491,"about_ca_topic_score_gemma":0.005571121,"domain_scores_codex":[0.9990466,0.0002505518,0.000047824,0.0002908673,0.0002525177,0.0001115373],"domain_scores_gemma":[0.9970846,0.001333898,0.0002729382,0.0008805513,0.0003215416,0.0001065834],"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":[0.0004368829,0.0003251942,0.006346893,0.0003169542,0.0001772184,0.0003466104,0.0002820821,0.2982826,0.01179777,0.01395039,0.02088289,0.6468546],"study_design_scores_gemma":[0.00001472473,0.00005051276,0.0003940804,0.00001821219,0.00001178099,0.00008328853,0.00001913595,0.9865043,0.002919484,0.007983786,0.001988115,0.00001264325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07489746,0.001468281,0.8898403,0.0009666482,0.0002721063,0.0003121058,0.001702699,0.02414809,0.006392227],"genre_scores_gemma":[0.7286291,0.0005370795,0.2493155,0.001007427,0.0001441918,0.0003026458,0.005650228,0.0007925369,0.01362128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004073974,"threshold_uncertainty_score":0.01362884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09003985377664396,"score_gpt":0.3286912127699921,"score_spread":0.2386513589933482,"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."}}