{"id":"W4400835738","doi":"10.20944/preprints202407.1498.v1","title":"An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Honeypot; Exploit; Resilience (materials science); Computer science; Embedded system; Hardware security module; Computer security; Denial-of-service attack; Field-programmable gate array; Internet of Things; Hardware Trojan; Trojan; The Internet; Cryptography; Operating system","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":[0.0003318998,0.0005247071,0.000417314,0.0006398857,0.0004124229,0.000575605,0.001163438,0.0007130149,0.001468574],"category_scores_gemma":[0.0006702352,0.0002247406,0.0002822127,0.0001857595,0.0004174116,0.001434872,0.000802602,0.0005278101,0.0003670718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002482516,"about_ca_system_score_gemma":0.0002571797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001505716,"about_ca_topic_score_gemma":0.0002711277,"domain_scores_codex":[0.9996331,0.00007050743,0.00003503016,0.00008399662,0.0001117629,0.00006562508],"domain_scores_gemma":[0.9994543,0.00009020382,0.00009460187,0.0001726144,0.0001343625,0.00005383173],"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":[0.0008149823,0.0004973072,0.00499361,0.0004013735,0.0001868767,0.001013735,0.000389232,0.01781984,0.7054118,0.01036019,0.005489913,0.2526212],"study_design_scores_gemma":[0.0001709171,0.003522218,0.007620051,0.00008164517,0.0002477824,0.002458231,0.0001501272,0.4360801,0.5180353,0.006850234,0.02466378,0.0001195249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4784735,0.001142772,0.4999774,0.0006331071,0.0004639289,0.000445339,0.0001385368,0.0103459,0.008379559],"genre_scores_gemma":[0.9519389,0.0001397384,0.04522,0.0001692279,0.00003627556,0.00009944031,0.00006432042,0.00005340432,0.002278624],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001468574,"threshold_uncertainty_score":0.004912853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06814324322137767,"score_gpt":0.3339636353568496,"score_spread":0.265820392135472,"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."}}