{"id":"W4389633672","doi":"10.1109/access.2023.3341911","title":"Malicious Data Classification in Packet Data Network Through Hybrid Meta Deep Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Intrusion detection system; Meta learning (computer science); Wireless network; Deep learning; Network packet; Data mining; Computer security; Computer network; Wireless; Telecommunications; Task (project management)","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":[0.001780386,0.0007519406,0.0006709251,0.001094416,0.0003665165,0.001166803,0.001451303,0.001027047,0.0005177978],"category_scores_gemma":[0.002672699,0.0004287047,0.000800899,0.0006218493,0.0007765176,0.002092868,0.001190961,0.001411198,0.0001471322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236051,"about_ca_system_score_gemma":0.0007744845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003496529,"about_ca_topic_score_gemma":0.0039006,"domain_scores_codex":[0.9994928,0.0001604396,0.00003320722,0.0001181986,0.0001072524,0.00008813613],"domain_scores_gemma":[0.998798,0.0005035654,0.0001434048,0.000170944,0.0003062244,0.00007789899],"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":[0.0002160754,0.000238611,0.006922206,0.00003939923,0.0001672036,0.0001056572,0.00009884176,0.8741636,0.005426138,0.005114888,0.0007405692,0.1067669],"study_design_scores_gemma":[0.000001207062,0.00001496137,0.00007405317,0.000001329458,0.000003327856,0.000004523864,0.00000370191,0.998616,0.0005770537,0.0006671491,0.00003513872,0.00000153399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2324158,0.0004683816,0.7635271,0.0006809838,0.00005168874,0.0000583361,0.00009484411,0.001191855,0.001511023],"genre_scores_gemma":[0.9358651,0.0001110052,0.06252645,0.0001632717,0.00001968482,0.00004610028,0.0001267836,0.00003149864,0.00111018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003496529,"threshold_uncertainty_score":0.009415746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2489178171268187,"score_gpt":0.3742276237767493,"score_spread":0.1253098066499307,"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."}}