{"id":"W4413992561","doi":"10.1007/978-3-031-94455-0_5","title":"Siamese Neural Network for Robust IoT Device-Type Identification: A Few-Shot Learning Approach","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Identification (biology); Shot (pellet); Computer science; Artificial neural network; Artificial intelligence; Type (biology); One shot; Single shot; Machine learning; Engineering; Physics; Biology; Materials science; Optics; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000823963,0.0008117758,0.001273559,0.0008358593,0.0003871067,0.0007968773,0.001657903,0.001466075,0.001595999],"category_scores_gemma":[0.001501468,0.0005045266,0.0008997155,0.0008766848,0.0006090326,0.001374766,0.0009787038,0.001482626,0.0006491201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006691212,"about_ca_system_score_gemma":0.0007954987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007985463,"about_ca_topic_score_gemma":0.007676288,"domain_scores_codex":[0.9996835,0.00005983671,0.00001915071,0.0001138158,0.000078738,0.00004489424],"domain_scores_gemma":[0.9993838,0.0003115662,0.00004818391,0.00007306377,0.0001555976,0.00002784472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001355319,0.0001836656,0.001029292,0.0001348958,0.0001572718,0.0001486206,0.00006314624,0.5676882,0.01216776,0.008751438,0.003969737,0.4055704],"study_design_scores_gemma":[6.926013e-7,0.000008199286,0.0001194292,0.000001679402,0.000005344008,0.00001395174,0.000002397766,0.9978912,0.0005560067,0.001268963,0.0001288135,0.000003170849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01741153,0.000837129,0.9795253,0.000220903,0.00007368095,0.00003269329,0.00008862379,0.000553248,0.001256812],"genre_scores_gemma":[0.7052086,0.001161604,0.2721938,0.0004956254,0.0002544962,0.0001519804,0.0007569619,0.0002325104,0.01954445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007985463,"threshold_uncertainty_score":0.01587796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03649025178603554,"score_gpt":0.2642721096861984,"score_spread":0.2277818579001628,"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."}}