{"id":"W4290859396","doi":"10.48550/arxiv.2208.05085","title":"Collaborative Feature Maps of Networks and Hosts for AI-driven Intrusion Detection","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada); University of Ottawa","funders":"","keywords":"Computer science; Host (biology); Data mining; Intrusion detection system; Benchmark (surveying); Variety (cybernetics); Feature (linguistics); Network security; Baseline (sea); Flow network; Artificial intelligence; Machine learning; Computer network","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":[0.0007811174,0.001275738,0.0006690367,0.001667933,0.0003637053,0.001027848,0.001680788,0.0009780555,0.002092113],"category_scores_gemma":[0.003616469,0.0003065916,0.0009494708,0.001409372,0.000469381,0.001837257,0.001378668,0.001460974,0.0009620439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00097911,"about_ca_system_score_gemma":0.0008537108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00716654,"about_ca_topic_score_gemma":0.007627946,"domain_scores_codex":[0.9994329,0.0001010612,0.00002510254,0.0002461786,0.000113544,0.00008124478],"domain_scores_gemma":[0.9991395,0.0003490155,0.00009141063,0.0001861192,0.0001742655,0.00005965985],"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":[0.0005495853,0.0006775586,0.01758182,0.000235605,0.00024121,0.0002653062,0.0001556267,0.373088,0.009561731,0.008596067,0.02262336,0.5664242],"study_design_scores_gemma":[0.000007683108,0.00004145131,0.001248383,0.00000738768,0.00001533176,0.00004330385,0.00002104552,0.9897749,0.002268366,0.004677486,0.001886145,0.000008558793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1489818,0.001960974,0.8251305,0.001218929,0.0003155693,0.0002665406,0.005439803,0.01052332,0.006162591],"genre_scores_gemma":[0.8616158,0.0003873983,0.1249259,0.0002795962,0.000128548,0.0002025279,0.008297638,0.0001848464,0.003977769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00716654,"threshold_uncertainty_score":0.01424962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927679851042873,"score_gpt":0.1837564892892636,"score_spread":0.1644796907788348,"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."}}