{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003997118,0.0001781185,0.0002019898,0.0004663809,0.00051941,0.001256288,0.003048319,0.00008127435,0.000008120505],"category_scores_gemma":[0.0001272667,0.0001457855,0.00003560537,0.001838313,0.0002392702,0.0044606,0.0008316757,0.0008006709,0.000007454931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002087689,"about_ca_system_score_gemma":0.0003337094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000126856,"about_ca_topic_score_gemma":0.00002145851,"domain_scores_codex":[0.9969482,0.0002036625,0.0005609589,0.0006450248,0.001290151,0.0003519337],"domain_scores_gemma":[0.9981756,0.0002881064,0.0002978446,0.0005477789,0.0004855945,0.0002051397],"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":[0.0001728341,0.00004457888,0.0000726361,0.0000114069,0.00002756183,0.00008200066,0.0002022487,0.5169649,0.000698627,0.01144988,0.001230956,0.4690423],"study_design_scores_gemma":[0.0002359385,0.0004461434,0.0001323484,0.0003820543,0.000009964578,0.0002415532,0.00002843852,0.985875,0.0001746974,0.001744489,0.01057364,0.0001557063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01385244,0.0002707575,0.9815924,0.0005211936,0.003360198,0.00008561673,0.00005068312,0.00008433938,0.0001823603],"genre_scores_gemma":[0.9573937,0.0001442801,0.04001031,0.0004384918,0.00189044,0.000001978282,0.0001064653,0.000009885367,0.000004468167],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9435412,"threshold_uncertainty_score":0.9997805,"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."}}