{"id":"W4294975739","doi":"10.1109/iri54793.2022.00053","title":"Dynamic Packet Filtering Using Machine Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Network packet; Computer science; Payload (computing); Firewall (physics); Deep packet inspection; Filter (signal processing); Artificial intelligence; The Internet; Network security; Packet generator; Packet analyzer; Data mining; Machine learning; Real-time computing; Computer network; Processing delay; Transmission delay; Computer vision; Entropy (arrow of time)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002232494,0.00005962818,0.00006288887,0.00007553426,0.0005995129,0.00007337919,0.0003118817,0.0000126963,0.0007197594],"category_scores_gemma":[0.000007174438,0.00006181881,0.00003460207,0.0003328451,0.000007515637,0.0002356407,0.0007062819,0.0002555647,0.00001304085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007158123,"about_ca_system_score_gemma":0.00001276193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001082976,"about_ca_topic_score_gemma":0.00002541033,"domain_scores_codex":[0.9992867,0.0000940031,0.0001006888,0.0001900376,0.0001736853,0.0001548729],"domain_scores_gemma":[0.9997242,0.00002191512,0.00004124331,0.000170328,0.00001048133,0.00003180654],"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":[0.00004830052,0.0002062798,0.001627805,0.00003593606,0.00005499384,0.0001365147,0.003631237,0.539422,0.1079236,0.04300865,0.0009282428,0.3029764],"study_design_scores_gemma":[0.00007608632,0.00006707387,0.00005066542,0.000001869527,0.000001025831,0.00006996201,0.00002927364,0.9767429,0.0005614096,0.0007707656,0.02154469,0.00008427283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2975411,0.0001382864,0.6992859,0.0002547968,0.000729997,0.00006053046,8.125484e-7,0.0003346199,0.001654012],"genre_scores_gemma":[0.9748426,0.00001034091,0.02418415,0.0002336163,0.00002097657,0.000004757098,0.000002147198,0.000005794204,0.0006955986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6773015,"threshold_uncertainty_score":0.7880861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522431105146665,"score_gpt":0.2339873289833902,"score_spread":0.2187630179319235,"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."}}