{"id":"W4413925826","doi":"10.1109/tdsc.2025.3605197","title":"WFCAT: Augmenting Website Fingerprinting With Channel-Wise Attention on Timing Features","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Research Foundation Singapore","keywords":"Computer science; Channel (broadcasting); Fingerprint (computing); Computer network; Computer security","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":[0.0003943833,0.001228695,0.0007930177,0.0008591629,0.0002896636,0.000650637,0.001044571,0.0008154942,0.001327411],"category_scores_gemma":[0.001569739,0.0002633875,0.0006123967,0.0005382051,0.0005063518,0.001838648,0.001046472,0.001433181,0.000708312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007369772,"about_ca_system_score_gemma":0.0009480232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006268058,"about_ca_topic_score_gemma":0.007637134,"domain_scores_codex":[0.9997118,0.00003355142,0.00001046983,0.00009033761,0.00007788487,0.00007587011],"domain_scores_gemma":[0.9993865,0.0001600534,0.0001056793,0.0001586177,0.0001310928,0.00005804976],"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":[0.0005376393,0.0005451734,0.0123461,0.0001559562,0.0002056669,0.000300838,0.0001232974,0.3353727,0.04572075,0.006920099,0.01556806,0.5822037],"study_design_scores_gemma":[0.000007194762,0.00007752527,0.001324525,0.000006287904,0.0000263546,0.00007965239,0.00001126804,0.9883479,0.006899414,0.002026729,0.001179344,0.00001373267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3115641,0.001299243,0.6610789,0.0008691536,0.0004035458,0.0001894701,0.001036499,0.01437489,0.009184221],"genre_scores_gemma":[0.9462393,0.0002898025,0.04684947,0.0003213259,0.00009281266,0.00006096749,0.001081194,0.0001660537,0.004899137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006268058,"threshold_uncertainty_score":0.01246315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009607949373425033,"score_gpt":0.2412968135845509,"score_spread":0.2316888642111259,"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."}}