{"id":"W4391931886","doi":"10.20944/preprints202402.0898.v1","title":"CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Information and Cyber Security","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Protocol (science); Computer science; Computer security; Health care; Computer network; Medicine; Political science","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":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002529379,0.0006939655,0.0007548423,0.0005767858,0.0002478118,0.000624326,0.003101891,0.0006499271,0.00008836811],"category_scores_gemma":[0.000251989,0.0007261948,0.0003006905,0.0007591172,0.00007667127,0.001028337,0.008729901,0.002266913,0.001277698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006018231,"about_ca_system_score_gemma":0.001090642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001269027,"about_ca_topic_score_gemma":0.001623433,"domain_scores_codex":[0.9947574,0.0003827444,0.001444474,0.001870317,0.0006722111,0.0008728485],"domain_scores_gemma":[0.9957848,0.000192603,0.0006416575,0.002719202,0.000345787,0.0003160129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003955955,0.003419034,0.7153534,0.08685349,0.001011101,0.0005343151,0.06825501,0.004862233,0.0005449257,0.0700658,0.01950105,0.02920405],"study_design_scores_gemma":[0.003884621,0.00008059719,0.2176046,0.004236698,0.0001139204,0.00006102159,0.0005906533,0.4066955,0.007125826,0.02161606,0.3342285,0.003761916],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5602315,0.0006311057,0.05664269,0.02267393,0.01284768,0.326445,0.009256131,0.003905778,0.007366208],"genre_scores_gemma":[0.9193116,0.000008980856,0.007869237,0.002209738,0.0002467066,0.06856753,0.001640833,0.00007077835,0.00007458237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4977488,"threshold_uncertainty_score":0.9995189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.221273884177981,"score_gpt":0.460110762285098,"score_spread":0.238836878107117,"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."}}