{"id":"W4391110914","doi":"10.1145/3641847","title":"Battling against Protocol Fuzzing: Protecting Networked Embedded Devices from Dynamic Fuzzers","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fuzz testing; Computer science; Protocol (science); Computer security; Obfuscation; Encryption; Code (set theory); Overhead (engineering); Embedded system; Operating system; Programming language; Software; Set (abstract data type)","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":[0.001959029,0.0009742197,0.0006655517,0.001185387,0.0006455951,0.001369582,0.001931242,0.001159784,0.001141914],"category_scores_gemma":[0.009971757,0.0004254289,0.0006881327,0.0003119367,0.001696486,0.004000071,0.002497439,0.001694421,0.0004018915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008368483,"about_ca_system_score_gemma":0.00117496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001199435,"about_ca_topic_score_gemma":0.001247914,"domain_scores_codex":[0.9976195,0.0005504186,0.0001909894,0.0004692257,0.0008455045,0.0003243879],"domain_scores_gemma":[0.9908321,0.002891237,0.001434001,0.003931129,0.0006934701,0.0002180122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001779491,0.000647067,0.01938433,0.0007249537,0.0003473883,0.001275638,0.001349119,0.1477825,0.2582674,0.05169127,0.006107071,0.5106437],"study_design_scores_gemma":[0.00006350717,0.000863725,0.002934565,0.0001538264,0.000167907,0.0009203161,0.0001506332,0.795432,0.1788956,0.01327044,0.007051953,0.00009553697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3390362,0.001898932,0.637582,0.0008364419,0.0001351533,0.0003632549,0.0001054639,0.01298721,0.007055262],"genre_scores_gemma":[0.9438505,0.0003243216,0.05391707,0.000285751,0.00002411911,0.00008047781,0.00007683759,0.000151493,0.001289455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001959029,"threshold_uncertainty_score":0.01036042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0505011007126762,"score_gpt":0.3374481481141943,"score_spread":0.2869470474015181,"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."}}