{"id":"W4360997187","doi":"10.1109/aiipcc57291.2022.00011","title":"An Intelligent Network Intrusion Detector Using Deep Learning Model","year":2022,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Intrusion detection system; Computer science; Decision tree; Vulnerability (computing); Network security; Scope (computer science); Computer security; Artificial intelligence; Set (abstract data type); Intrusion; Control (management); Tree (set theory); Machine learning; Data mining","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":[0.0005541854,0.0007075604,0.0008042617,0.000760413,0.000337689,0.0006183184,0.001183761,0.0008447256,0.001259037],"category_scores_gemma":[0.0008804012,0.0003915581,0.0006487255,0.000636834,0.0002069666,0.001119808,0.0006341901,0.00131394,0.0004409296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008216313,"about_ca_system_score_gemma":0.0009648298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009480715,"about_ca_topic_score_gemma":0.01086007,"domain_scores_codex":[0.9997632,0.00002851281,0.00001435167,0.00006961774,0.00007086068,0.00005351718],"domain_scores_gemma":[0.9997017,0.00008497993,0.00002608713,0.00002180863,0.000144683,0.00002069148],"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":[0.0002625914,0.0003717816,0.005774748,0.0001047744,0.0002249936,0.0002071992,0.00004831237,0.5253981,0.01416557,0.004221687,0.01027746,0.4389429],"study_design_scores_gemma":[0.000003368467,0.00001409991,0.000174419,0.000002690265,0.000008495294,0.00001339832,0.000001524875,0.9979809,0.001065776,0.0004422648,0.0002893848,0.000003568065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08082876,0.001209057,0.9068755,0.0007406626,0.0002944119,0.0001070083,0.0005487935,0.005107558,0.004288258],"genre_scores_gemma":[0.766493,0.0008315184,0.2208061,0.0005891941,0.0001095699,0.000165854,0.001446418,0.0001168777,0.009441417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009480715,"threshold_uncertainty_score":0.01885104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347611902916412,"score_gpt":0.2520034362456263,"score_spread":0.2285273172164622,"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."}}