{"id":"W4406134911","doi":"10.1007/978-3-031-78806-2_3","title":"On IT and OT Cybersecurity Datasets for Machine Learning-Based Intrusion Detection in Industrial Control Systems","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Intrusion detection system; Computer science; Computer security; Industrial control system; Intrusion prevention system; Intrusion; Control (management); Artificial intelligence; Geology","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":[0.00116797,0.001334836,0.000650301,0.003963377,0.0006080875,0.001049311,0.001182011,0.001091431,0.003996828],"category_scores_gemma":[0.004365683,0.0002037853,0.001264305,0.003307265,0.0003732665,0.001203666,0.001013399,0.001349359,0.003086096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000867251,"about_ca_system_score_gemma":0.0008819686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02337465,"about_ca_topic_score_gemma":0.03843956,"domain_scores_codex":[0.9989254,0.0001897106,0.000116751,0.0002804057,0.0003582023,0.0001295797],"domain_scores_gemma":[0.9979632,0.0007162145,0.0001574318,0.0004822183,0.0005257392,0.000155293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001423367,0.00239216,0.05917124,0.001320154,0.0005663813,0.0006486077,0.0002506325,0.04481319,0.007072148,0.004143563,0.5205109,0.3576877],"study_design_scores_gemma":[0.0006935819,0.001351874,0.2472941,0.0005889472,0.0003921989,0.00163895,0.001612569,0.485194,0.02194914,0.009510422,0.2295778,0.0001963741],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4861086,0.003593404,0.01562542,0.002221767,0.001224038,0.0005543405,0.4653494,0.009746312,0.01557664],"genre_scores_gemma":[0.2190107,0.0006866059,0.02161623,0.000248169,0.0001618221,0.0002849229,0.752451,0.0002341506,0.005306449],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02337465,"threshold_uncertainty_score":0.0464772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976083052134387,"score_gpt":0.232412859019961,"score_spread":0.2126520284986172,"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."}}