{"id":"W2982698051","doi":"10.1109/tvlsi.2019.2947202","title":"Incremental Fault Analysis: Relaxing the Fault Model of Differential Fault Attacks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fault injection; Cryptosystem; Fault (geology); Advanced Encryption Standard; Computer science; Block cipher; Cryptography; Fault model; Encryption; Embedded system; Algorithm; Computer security; Engineering; Software; Geology; Operating system; Seismology; Electrical engineering","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":[0.0008049312,0.0007414557,0.0005864115,0.001112624,0.0003484617,0.0005888523,0.001575487,0.0006844593,0.001328086],"category_scores_gemma":[0.00402453,0.0002876741,0.0006748967,0.000469562,0.001256884,0.003119972,0.0009579906,0.001313598,0.0002921612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006579561,"about_ca_system_score_gemma":0.0007566997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009649962,"about_ca_topic_score_gemma":0.0009046195,"domain_scores_codex":[0.9988483,0.0002326399,0.00006699135,0.0001825,0.000538393,0.0001310872],"domain_scores_gemma":[0.9966276,0.001425967,0.0004150516,0.00102494,0.0004381992,0.00006824349],"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":[0.0004808311,0.0001642768,0.004889775,0.0003617078,0.00009774604,0.0005876155,0.000412144,0.5273075,0.06805051,0.1300545,0.002335928,0.2652575],"study_design_scores_gemma":[0.00002190057,0.0002653954,0.000389291,0.0000245816,0.00003723871,0.000345098,0.00002491772,0.9431911,0.01701519,0.03494503,0.00371132,0.00002880012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02402537,0.0002148943,0.9733552,0.0001511842,0.00005580304,0.00005021558,0.00003528393,0.0006868894,0.001425229],"genre_scores_gemma":[0.8163527,0.0004284403,0.1809356,0.0001836266,0.0001276391,0.00008822079,0.00009972844,0.0001655314,0.00161837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001575487,"threshold_uncertainty_score":0.004773855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775955824930268,"score_gpt":0.2658511145248987,"score_spread":0.248091556275596,"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."}}