{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001386541,0.0007433001,0.001049303,0.0019747,0.0005552214,0.001438698,0.001142361,0.001039178,0.001302435],"category_scores_gemma":[0.003173207,0.0004061288,0.001000295,0.00114993,0.000599385,0.001644401,0.0006514545,0.001117208,0.0004478934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001344759,"about_ca_system_score_gemma":0.001044123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008965303,"about_ca_topic_score_gemma":0.004874929,"domain_scores_codex":[0.9991259,0.0001595566,0.00007073682,0.0002481366,0.0002921554,0.0001034954],"domain_scores_gemma":[0.9984067,0.0008740627,0.0001606302,0.0001984548,0.0003263011,0.00003381683],"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":[0.0001096375,0.0001800142,0.003327768,0.00007615398,0.0001243571,0.0001240629,0.00005522332,0.691032,0.003732451,0.009460246,0.001624542,0.2901536],"study_design_scores_gemma":[0.000002673636,0.00001232219,0.0001446198,0.000004939985,0.000007430655,0.00001386941,0.0000031219,0.9955942,0.0008012218,0.003007452,0.0004035868,0.000004613737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01436906,0.0003741637,0.9814105,0.0001991189,0.00006396911,0.0000551485,0.0000636273,0.001624974,0.001839456],"genre_scores_gemma":[0.7252227,0.0009775796,0.2674487,0.0003042581,0.0001625724,0.0002130588,0.0004946652,0.0001491918,0.005027336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008965303,"threshold_uncertainty_score":0.01782626,"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."}}