{"id":"W4405181837","doi":"10.1145/3658644.3690307","title":"OctopusTaint: Advanced Data Flow Analysis for Detecting Taint-Based Vulnerabilities in IoT/IIoT Firmware","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Taint checking; Firmware; Computer security; False positive paradox; False positives and false negatives; Vulnerability (computing); Software; Operating system; Artificial intelligence","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":[0.0008035528,0.001194117,0.0003804726,0.002688204,0.0003965894,0.00102107,0.0009730282,0.0005140819,0.002289841],"category_scores_gemma":[0.003231108,0.0003262908,0.0006230337,0.00104152,0.0006435828,0.002521611,0.0009640137,0.000821501,0.0005579141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007102476,"about_ca_system_score_gemma":0.001510013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003213831,"about_ca_topic_score_gemma":0.004031848,"domain_scores_codex":[0.9991744,0.0001072923,0.00007540538,0.0001602607,0.000401666,0.00008091791],"domain_scores_gemma":[0.9981557,0.0007804525,0.0003402308,0.0003225793,0.0003327705,0.00006826215],"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":[0.0009844049,0.0005765015,0.06465915,0.001429854,0.0002361168,0.001282457,0.001535288,0.04723868,0.08405229,0.01857111,0.0282958,0.7511383],"study_design_scores_gemma":[0.0001251413,0.0007769236,0.01618593,0.0002749441,0.0001385629,0.001194827,0.0004355796,0.7809656,0.1328582,0.02387573,0.04297471,0.0001939456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2134552,0.001698916,0.6603051,0.0005849378,0.0002332071,0.0006274186,0.004548678,0.1105674,0.007979285],"genre_scores_gemma":[0.6863078,0.0008181104,0.2970464,0.0004428521,0.00007227345,0.0004186891,0.007085609,0.002895524,0.00491268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003213831,"threshold_uncertainty_score":0.00766027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03170630715243451,"score_gpt":0.3221118025548699,"score_spread":0.2904054954024354,"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."}}