{"id":"W4391012104","doi":"10.5267/j.ijdns.2024.1.007","title":"An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset","year":2024,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Support vector machine; Intrusion detection system; Machine learning; Data mining; Feature (linguistics); Feature selection; Feature extraction; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001039553,0.00197302,0.0009039975,0.001944887,0.0004972405,0.0008942866,0.002743264,0.001123182,0.001448815],"category_scores_gemma":[0.001787729,0.0003323453,0.0009256233,0.001344259,0.0003361536,0.001480408,0.001124554,0.001589046,0.001159649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001503819,"about_ca_system_score_gemma":0.001363538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02856235,"about_ca_topic_score_gemma":0.03509394,"domain_scores_codex":[0.9994055,0.0001044121,0.00004763022,0.0002020287,0.0001456845,0.00009468444],"domain_scores_gemma":[0.9994683,0.0001033942,0.00006172431,0.0001259675,0.0001891808,0.00005142667],"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":[0.00143923,0.002353617,0.04634096,0.0007357836,0.0006878281,0.0006366774,0.0001689429,0.2604531,0.01563428,0.003950769,0.1864033,0.4811955],"study_design_scores_gemma":[0.00005991857,0.0001883593,0.005711274,0.00002814896,0.00004734167,0.0001130308,0.00005168812,0.9774284,0.007018258,0.001403619,0.00791113,0.0000388479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6751226,0.003787166,0.1726941,0.002755898,0.001191317,0.001192177,0.09886005,0.0305133,0.01388342],"genre_scores_gemma":[0.6681519,0.0009266868,0.1284683,0.0006038773,0.0001350584,0.0008469787,0.1922484,0.0003267082,0.008292042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02856235,"threshold_uncertainty_score":0.0567922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01994087122665221,"score_gpt":0.3016464099297404,"score_spread":0.2817055387030882,"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."}}