{"id":"W4408519585","doi":"10.1109/lnet.2025.3551989","title":"Comprehensive Advanced Persistent Threats Dataset","year":2025,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011772,0.0001741752,0.0001842989,0.0001159888,0.0003198769,0.0001696281,0.0007102251,0.00006388667,0.000008073007],"category_scores_gemma":[0.000002927414,0.0001770432,0.0001149513,0.0005310336,0.00006346025,0.0002552245,0.0001918232,0.0002558145,0.00004582769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007920565,"about_ca_system_score_gemma":0.00002043518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001672347,"about_ca_topic_score_gemma":0.00001066819,"domain_scores_codex":[0.9986135,0.00009236975,0.0002196321,0.0004867167,0.0002054937,0.0003822718],"domain_scores_gemma":[0.9990336,0.0001338283,0.00008102892,0.0006538154,0.00003639663,0.00006132323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000563813,0.00005191,0.0001859292,0.00004341736,0.0001320559,0.00005980666,0.0002988818,0.05152326,0.01020965,0.002111677,0.5117432,0.4235838],"study_design_scores_gemma":[0.0006544015,0.00007278013,0.0003117392,0.0002010395,0.00002670166,0.00002170633,0.00001710297,0.08258725,0.001648056,0.001031813,0.9130645,0.0003628911],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1941706,0.005870255,0.7264502,0.02988257,0.03707594,0.001012465,0.00004294443,0.00123943,0.004255597],"genre_scores_gemma":[0.9393249,0.0004322574,0.007546387,0.05114223,0.001178702,0.00004175757,0.00007655453,0.00001860887,0.0002386714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7451543,"threshold_uncertainty_score":0.7219612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02316646067264357,"score_gpt":0.2621675295900745,"score_spread":0.2390010689174309,"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."}}