{"id":"W2147163399","doi":"10.1109/aina.2008.44","title":"An Experience Improving Intrusion Detection Systems False Alarm Ratio by Using Honeypot","year":2008,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Honeypot; Intrusion detection system; Computer science; Firewall (physics); ALARM; Constant false alarm rate; Computer security; Scheme (mathematics); False alarm; Network security; Artificial intelligence; Engineering","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":[0.0002424433,0.0001724125,0.0001692309,0.0001231192,0.000850994,0.0002772147,0.0004737867,0.0001289465,0.00001980143],"category_scores_gemma":[0.00002403747,0.0001618197,0.00004545119,0.0005289391,0.0000597265,0.002272639,0.000147115,0.0001795868,0.00002247492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000114982,"about_ca_system_score_gemma":0.0000417237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001093957,"about_ca_topic_score_gemma":0.00004183491,"domain_scores_codex":[0.9983242,0.0001425423,0.0003301128,0.0005261716,0.000366093,0.0003109012],"domain_scores_gemma":[0.9990154,0.00002899522,0.0001449578,0.0005400832,0.0001154332,0.0001551251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001476361,0.00007912915,0.00006298517,0.0000129327,0.000003705776,0.00001776368,0.001992268,0.00204839,0.9435343,0.0009094319,0.0001268024,0.0511975],"study_design_scores_gemma":[0.0001549257,0.0001594384,0.00003331246,0.00001452035,0.000001936522,0.0003210984,0.0001359293,0.8274072,0.1704698,0.00003545769,0.001065565,0.0002008589],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4872802,0.0001667709,0.5112643,0.00001268705,0.0008419825,0.0001274391,2.802716e-7,0.0002397699,0.00006659559],"genre_scores_gemma":[0.9956333,0.00005623214,0.003798654,0.0001347802,0.0002362716,0.00002008125,0.00000139792,0.00001164957,0.0001076569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8253588,"threshold_uncertainty_score":0.6598815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952908124413811,"score_gpt":0.2386999822589175,"score_spread":0.2191709010147794,"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."}}