{"id":"W4415222043","doi":"10.1109/tfuzz.2025.3621833","title":"PAC-X: Fuzzy Explainable AI for Multiclass Malware Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McGill University","funders":"Canada Research Chairs; Defence Research and Development Canada","keywords":"Malware; Adversarial system; Exploit; Robustness (evolution); Embedding; Fuzzy logic; Cluster analysis; Artificial neural network","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":[0.0002707255,0.0002227359,0.00024436,0.0003936573,0.0007794534,0.0002689261,0.0004992858,0.0001898056,0.000002430404],"category_scores_gemma":[0.000006095639,0.0002250951,0.0002039551,0.000893103,0.00003877472,0.0004190801,0.000004095089,0.000241627,0.00004418153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002344431,"about_ca_system_score_gemma":0.00007951725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002486467,"about_ca_topic_score_gemma":0.00008868502,"domain_scores_codex":[0.9983972,0.00007296497,0.000408939,0.0005684316,0.0001982002,0.0003542237],"domain_scores_gemma":[0.9986168,0.000157289,0.0001047989,0.0007789154,0.0002447716,0.00009747429],"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":[0.0002722289,0.001640013,0.00003805299,0.001229446,0.0005099187,0.00001176259,0.0009304371,0.07405844,0.07408662,0.1577455,0.01547672,0.6740009],"study_design_scores_gemma":[0.001830854,0.0007182767,0.00008710473,0.0002863273,0.0001114158,0.00005931628,0.0006797221,0.3835844,0.4439754,0.008684135,0.159034,0.0009490828],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006756532,0.00006582768,0.9916883,0.0008546531,0.001676408,0.001401499,0.00003395519,0.001023378,0.002580291],"genre_scores_gemma":[0.9828984,0.00001882905,0.005935592,0.0002641098,0.00006613536,0.002863891,0.000001672294,0.00002031377,0.007931022],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9857528,"threshold_uncertainty_score":0.9179111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300082576176146,"score_gpt":0.2631470465305248,"score_spread":0.2501462207687633,"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."}}