{"id":"W3181232393","doi":"10.1109/access.2021.3093830","title":"Building an Intrusion Detection System to Detect Atypical Cyberattack Flows","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Machine learning; Hyperparameter; Intrusion detection system; Feature selection; Attack model; Decision tree; Artificial neural network; Deep learning; Data mining; Computer security","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":[0.0009290321,0.001029062,0.0007175865,0.001240605,0.0003804836,0.0006631343,0.0009258285,0.0007479172,0.0007276533],"category_scores_gemma":[0.001434194,0.0003585884,0.0004758661,0.0004065072,0.0002736353,0.001333917,0.000865531,0.001034059,0.0004788387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006807175,"about_ca_system_score_gemma":0.0007600554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003049202,"about_ca_topic_score_gemma":0.003050322,"domain_scores_codex":[0.9996475,0.00005583367,0.00003106956,0.0001196026,0.00009679031,0.00004916663],"domain_scores_gemma":[0.9995306,0.0001138375,0.00006691543,0.00006800738,0.0001819785,0.00003871198],"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":[0.0004763138,0.0008940697,0.0312615,0.0002050101,0.0003618271,0.0006445398,0.0002061159,0.277756,0.0934458,0.003671561,0.009214416,0.5818628],"study_design_scores_gemma":[0.000006222148,0.00007482263,0.001340109,0.000005253033,0.00001832405,0.00006305921,0.0000093271,0.9849406,0.0122861,0.0005186968,0.0007287711,0.000008752872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3274801,0.0003835209,0.6476523,0.000539032,0.0002307018,0.0004459164,0.0005801753,0.01963666,0.003051532],"genre_scores_gemma":[0.7994509,0.0001718135,0.1972373,0.0002085922,0.0000373663,0.0001661143,0.0008698567,0.00006938579,0.001788673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003049202,"threshold_uncertainty_score":0.006062865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02388061751377095,"score_gpt":0.2974029380763975,"score_spread":0.2735223205626265,"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."}}