{"id":"W2116055298","doi":"10.1109/tnsm.2011.050311.100028","title":"Dirichlet-Based Trust Management for Effective Collaborative Intrusion Detection Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Korea Science and Engineering Foundation; Nanyang Technological University","keywords":"Computer science; Intrusion detection system; Scalability; Robustness (evolution); Trust management (information system); Trustworthiness; Collaborative network; Network security; Computer network; Latent Dirichlet allocation; Distributed computing; Data mining; Computer security; Artificial intelligence; Topic model; Database","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"],"consensus_categories":[],"category_scores_codex":[0.0004768161,0.0003629688,0.000280741,0.0002194928,0.0009502224,0.0001556465,0.0004260398,0.0001570088,0.00003199127],"category_scores_gemma":[6.642113e-7,0.000357669,0.0001207144,0.001649518,0.00004298997,0.0003681132,0.00002369208,0.0002586893,0.0000190771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155898,"about_ca_system_score_gemma":0.00001010378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003389299,"about_ca_topic_score_gemma":0.000315556,"domain_scores_codex":[0.9978632,0.0001745842,0.0003649032,0.0007835915,0.0002786192,0.0005351314],"domain_scores_gemma":[0.9988505,0.000127624,0.0001539152,0.0005603621,0.0001540854,0.0001534792],"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":[0.0007670186,0.0003223293,0.000005344166,0.0001911187,0.0002747169,0.00001492875,0.0005233718,0.1669595,0.00001065546,0.005542392,0.0003637005,0.8250249],"study_design_scores_gemma":[0.002248443,0.001063833,0.001007934,0.0001763555,0.0002319554,0.000004648146,0.0002130138,0.9713739,0.001865455,0.002455906,0.01877175,0.000586788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002538104,0.00009681224,0.9878949,0.0002702976,0.002000995,0.002407022,0.000004423904,0.0003426161,0.004444845],"genre_scores_gemma":[0.9614219,0.0008643834,0.03290907,0.002755784,0.000204615,0.001627665,0.000005340373,0.00003946342,0.0001717853],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9588838,"threshold_uncertainty_score":0.9998875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009821254677230405,"score_gpt":0.2082034688065235,"score_spread":0.1983822141292931,"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."}}