{"id":"W4411446663","doi":"10.1109/tnsm.2025.3581463","title":"THREATIFY: APT Threat Variant Generation Using Graph-Based Machine Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Terrorism, Counterterrorism, and Political Violence","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Ericsson (Canada)","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Theoretical computer 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":[0.001008531,0.002123036,0.0007304939,0.002993572,0.0006040729,0.0008719304,0.001924496,0.00128767,0.00196738],"category_scores_gemma":[0.004709568,0.0004514711,0.00180386,0.001196202,0.0006090022,0.001622294,0.001196206,0.001572384,0.00083228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090285,"about_ca_system_score_gemma":0.001270347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007607061,"about_ca_topic_score_gemma":0.01181785,"domain_scores_codex":[0.9988537,0.0002424585,0.00005793399,0.0003452627,0.0003998311,0.0001007233],"domain_scores_gemma":[0.9972863,0.001492513,0.0002445089,0.0005085856,0.0003693742,0.00009867414],"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":[0.0002529876,0.0006225353,0.01649828,0.0003917772,0.0002730836,0.0005395271,0.0002071985,0.5232437,0.009936993,0.005774692,0.01787642,0.4243829],"study_design_scores_gemma":[0.00002257195,0.00007825832,0.0007223372,0.00001087334,0.00002814874,0.00008844527,0.00002641179,0.9897084,0.00235609,0.005138501,0.001806268,0.00001366284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1535621,0.001029839,0.8018775,0.0008846645,0.0002663268,0.0009533733,0.004206165,0.03170767,0.005512287],"genre_scores_gemma":[0.4889302,0.000330516,0.4915589,0.000471419,0.00007069096,0.0004809799,0.01414607,0.0007703733,0.003240935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007607061,"threshold_uncertainty_score":0.01512557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03123221297614175,"score_gpt":0.2933736506761007,"score_spread":0.2621414376999589,"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."}}