{"id":"W4289529959","doi":"10.3390/s22155690","title":"Towards an Explainable Universal Feature Set for IoT Intrusion Detection","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"","keywords":"Intrusion detection system; Internet of Things; Computer science; Feature selection; Classifier (UML); Artificial intelligence; Machine learning; Set (abstract data type); Intrusion; Data mining; Computer security","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":[0.002494338,0.001215348,0.0009695787,0.003013619,0.0006282919,0.00138149,0.001643053,0.001063095,0.001777341],"category_scores_gemma":[0.009573197,0.0003661905,0.001652831,0.001509334,0.0007813598,0.002152264,0.001602209,0.001840453,0.0003473693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001257812,"about_ca_system_score_gemma":0.001449649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003918086,"about_ca_topic_score_gemma":0.003608007,"domain_scores_codex":[0.9981255,0.0006005916,0.0002056605,0.0004709045,0.0004434807,0.0001539143],"domain_scores_gemma":[0.9956679,0.002405464,0.0004713949,0.0005925084,0.0007534256,0.0001091345],"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":[0.0006038535,0.0006319371,0.02498259,0.0005152207,0.0005750027,0.000767487,0.0007697747,0.2648992,0.01265391,0.06413481,0.007959521,0.6215066],"study_design_scores_gemma":[0.00003213176,0.0001177578,0.003035157,0.00005550044,0.00007712972,0.00009928364,0.0001000943,0.9518659,0.002777064,0.03937155,0.002435004,0.0000334607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06323793,0.000310572,0.932358,0.0005620969,0.00004618385,0.000162977,0.001139778,0.001400551,0.0007818121],"genre_scores_gemma":[0.6047303,0.0002283016,0.3889493,0.000222848,0.00008230944,0.0004527166,0.004381876,0.00008843788,0.0008638802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003918086,"threshold_uncertainty_score":0.01319146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391050634480076,"score_gpt":0.234885354034679,"score_spread":0.2209748476898782,"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."}}