{"id":"W4365443444","doi":"10.1109/tnnls.2023.3262981","title":"CapsRule: Explainable Deep Learning for Classifying Network Attacks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency","keywords":"Computer science; Artificial intelligence; Data mining; Fidelity; Artificial neural network; Machine learning; Denial-of-service attack; False positive paradox; Deep learning; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001040514,0.0002953145,0.0003606767,0.0002067696,0.002589293,0.0005634056,0.0003095188,0.0002433456,0.000009342601],"category_scores_gemma":[0.00001890143,0.0002893842,0.0001621736,0.00114811,0.00005049132,0.0005171917,0.00001100736,0.001270322,0.00002261662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005789362,"about_ca_system_score_gemma":0.00001476991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006225314,"about_ca_topic_score_gemma":0.00002600861,"domain_scores_codex":[0.9973154,0.0004933624,0.0004292039,0.0006579564,0.0002753771,0.0008287168],"domain_scores_gemma":[0.9984182,0.0008295857,0.000192721,0.0002779148,0.00009635358,0.0001852329],"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":[0.000035498,0.00001298552,0.00005029882,0.00004692794,0.00002889521,0.000009199274,0.0002859905,0.9154633,0.00002631341,0.0003184104,0.0006568693,0.08306528],"study_design_scores_gemma":[0.0004326077,0.0004507643,0.00008929167,0.0001195678,0.00001904149,0.00005137605,0.0002446486,0.9710448,0.00001767119,0.00004077056,0.02717751,0.0003119109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01913778,0.0007074235,0.9739322,0.0001975124,0.004274437,0.0004966352,5.98787e-7,0.001109407,0.0001439842],"genre_scores_gemma":[0.9955389,0.0005194642,0.0003449058,0.00009956699,0.0008360255,0.0002252514,0.000006211614,0.00004545402,0.002384203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9764012,"threshold_uncertainty_score":0.9999558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212800993239915,"score_gpt":0.2447008581831245,"score_spread":0.223420758859133,"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."}}