{"id":"W3089941628","doi":"10.1109/ijcnn48605.2020.9207066","title":"Detection of Malicious SCADA Communications via Multi-Subspace Feature Selection","year":2020,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"SCADA; Feature selection; Computer science; Intrusion detection system; Redundancy (engineering); Subspace topology; Data mining; Feature (linguistics); Artificial intelligence; Feature vector; Pipeline (software); Pattern recognition (psychology); Machine learning; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001121756,0.00009432907,0.0001182598,0.0000620091,0.0002031919,0.00004542855,0.0006092904,0.0001033353,0.00001453722],"category_scores_gemma":[0.0000307704,0.00009109978,0.00006218761,0.0009136696,0.00003940642,0.0003818781,0.0002394702,0.000254461,0.00003434352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000303262,"about_ca_system_score_gemma":0.00001921923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000168262,"about_ca_topic_score_gemma":0.000492532,"domain_scores_codex":[0.9992136,0.0001070256,0.0001631617,0.0002251374,0.0001594143,0.0001316646],"domain_scores_gemma":[0.9991944,0.00004055307,0.0001130092,0.0004367092,0.0001333491,0.00008200978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006960182,0.000300864,0.0005950923,0.0000645613,0.00006883591,0.000001646572,0.003275314,0.001287126,0.6803191,0.01394213,0.004727379,0.2953484],"study_design_scores_gemma":[0.0002358413,0.0001835209,0.0008128945,0.000006725532,0.000006321445,0.00001696164,0.00002550041,0.8421845,0.1412983,0.0001922995,0.0149251,0.0001120533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009426059,0.0002106668,0.9839311,0.005239291,0.000174299,0.0001602065,6.018115e-7,0.0003111446,0.0005466606],"genre_scores_gemma":[0.941608,0.00009378557,0.05734519,0.0007035349,0.00006425023,0.000008937972,0.000001423378,0.000006365382,0.0001684838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.932182,"threshold_uncertainty_score":0.3714941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364113847184884,"score_gpt":0.2475270742708414,"score_spread":0.2238859357989926,"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."}}