{"id":"W2113254798","doi":"10.1109/iaw.2004.1437799","title":"Searching covert channels by identifying malicious subjects in the time domain","year":2005,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Communication source; Covert; Covert channel; Computer science; Object (grammar); Channel (broadcasting); Computer security; Vulnerability (computing); Computer network; Artificial intelligence; Cloud computing security; Operating system; Security information and event management","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":[0.0007140579,0.0005230004,0.0008150541,0.00243922,0.0006908745,0.001012557,0.0004721435,0.0006285596,0.001340094],"category_scores_gemma":[0.004209247,0.0001867154,0.0004530412,0.00159763,0.0006441235,0.002410916,0.0007892076,0.0003219929,0.0003778696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003519733,"about_ca_system_score_gemma":0.0006869894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007877114,"about_ca_topic_score_gemma":0.001060742,"domain_scores_codex":[0.9991356,0.0002166118,0.00005348312,0.0001642777,0.0003099864,0.0001200525],"domain_scores_gemma":[0.9970254,0.001516164,0.0004959562,0.0003529035,0.0004708583,0.0001386119],"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":[0.002276104,0.0003544554,0.02679848,0.0003758986,0.0002005372,0.001579907,0.001739365,0.04371992,0.1584633,0.0455532,0.001589497,0.7173494],"study_design_scores_gemma":[0.00009985024,0.0008870973,0.01402062,0.0000425314,0.0001983462,0.002893875,0.0009427918,0.8278288,0.1105473,0.03574808,0.006674775,0.0001159408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4499432,0.0003501825,0.543855,0.0001884589,0.00004224094,0.0001558587,0.0001202973,0.0005476786,0.004796971],"genre_scores_gemma":[0.8872479,0.0002337611,0.1094005,0.00003511022,0.00004894579,0.00006028915,0.0001392648,0.0000330224,0.002801287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00243922,"threshold_uncertainty_score":0.004483044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243519590458624,"score_gpt":0.2520808500080772,"score_spread":0.2396456541034909,"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."}}