{"id":"W3039983497","doi":"10.1109/temc.2020.3003728","title":"On-Chip Magnetic Probes for Hardware Trojan Prevention and Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Electromagnetic Compatibility","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Trojan; Signature (topology); Computer hardware; Chip; Hardware Trojan; Embedded system; Computer science; Hardware security module; Cryptography; Telecommunications; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002096897,0.0004372229,0.0003283804,0.0004655547,0.0002270838,0.0003380845,0.0005821913,0.0006237645,0.002064569],"category_scores_gemma":[0.0008815638,0.0002179281,0.0001731248,0.0003345097,0.0003763466,0.0007850309,0.0003382194,0.0003472486,0.000678078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003679782,"about_ca_system_score_gemma":0.0002438349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001673978,"about_ca_topic_score_gemma":0.0004400065,"domain_scores_codex":[0.9997705,0.00004677886,0.000006083465,0.00002938416,0.0001276333,0.00001971693],"domain_scores_gemma":[0.9995425,0.0001478995,0.0001140534,0.0000892707,0.00008915661,0.00001719569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003412893,0.00009644417,0.001786218,0.0005229518,0.00003618216,0.0002215496,0.0001919277,0.007277906,0.8326229,0.01219097,0.003545544,0.1411661],"study_design_scores_gemma":[0.00008882872,0.001495983,0.004850166,0.000100089,0.0001119573,0.001398935,0.0001323235,0.104774,0.8230254,0.00387612,0.06008067,0.00006564558],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2519805,0.004943216,0.7146436,0.0009132352,0.0005512052,0.000272112,0.0002845393,0.003915458,0.02249622],"genre_scores_gemma":[0.87997,0.001314827,0.1132027,0.0002805342,0.0000525477,0.00007944529,0.0001115217,0.00009991011,0.0048886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002064569,"threshold_uncertainty_score":0.006906629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727829814669087,"score_gpt":0.2338212292593556,"score_spread":0.2165429311126647,"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."}}