{"id":"W3022318665","doi":"","title":"A deep unsupervised representation learning approach for effective cyber-physical attack detection and identification on highly imbalanced data.","year":2019,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Identification (biology); Artificial intelligence; Unsupervised learning; Feature learning; Representation (politics); Machine learning; Deep learning; Cyber-physical system; Pattern recognition (psychology)","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":[0.001603575,0.000712015,0.0009002266,0.001027231,0.0004309289,0.0009735303,0.001434787,0.001043612,0.001325617],"category_scores_gemma":[0.003062287,0.0003956459,0.0007613422,0.000977053,0.0004720238,0.001950182,0.001482693,0.002317498,0.0007204874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006020757,"about_ca_system_score_gemma":0.001038575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004510139,"about_ca_topic_score_gemma":0.007361992,"domain_scores_codex":[0.9994006,0.0002032186,0.00003850248,0.0001339421,0.0001154732,0.0001083459],"domain_scores_gemma":[0.9987959,0.0005330129,0.0001258153,0.0002080674,0.0002622552,0.00007485358],"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":[0.0003987829,0.0005732581,0.005729018,0.0001131075,0.0002398952,0.0001424747,0.0001496947,0.2474135,0.01081378,0.01037509,0.02098344,0.703068],"study_design_scores_gemma":[0.000005200965,0.00002988659,0.0004705084,0.000004860285,0.00001042398,0.00001316656,0.00001469413,0.9946147,0.0008711265,0.003441658,0.0005196834,0.000004179747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07003676,0.001496552,0.9222478,0.001179231,0.0002351684,0.00007272204,0.0005841305,0.00213506,0.002012657],"genre_scores_gemma":[0.762062,0.0005707313,0.2277736,0.000425513,0.0002778399,0.0001317816,0.002651971,0.000139558,0.005967072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004510139,"threshold_uncertainty_score":0.008967757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0900765946049906,"score_gpt":0.409323931078811,"score_spread":0.3192473364738204,"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."}}