{"id":"W2887371428","doi":"10.5539/cis.v11n3p67","title":"Machine Learning Approach to Combat False Alarms in Wireless Intrusion Detection System","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Constant false alarm rate; Intrusion detection system; False alarm; Wireless; Computer security; ALARM; Wireless network; False positive rate; Intrusion; Artificial intelligence; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001215815,0.0001245761,0.000139282,0.0006808236,0.0006241497,0.0006604615,0.0005989693,0.00005696905,0.0000014712],"category_scores_gemma":[0.00002536032,0.0001102545,0.00001999314,0.002131703,0.0001558483,0.00709738,0.0006258588,0.000192768,0.000075763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117645,"about_ca_system_score_gemma":0.0000377481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005908724,"about_ca_topic_score_gemma":0.000008637238,"domain_scores_codex":[0.9985608,0.00006195144,0.0003572503,0.0003018477,0.0004300633,0.0002880165],"domain_scores_gemma":[0.999204,0.00002525717,0.0001161323,0.0002625194,0.0002390088,0.0001531245],"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":[0.00002253391,0.00002950892,0.0003580776,0.00004898925,0.000001837737,5.203773e-7,0.006488716,0.002557096,0.001373182,0.04335905,0.00004322698,0.9457173],"study_design_scores_gemma":[0.0002454464,0.0002280877,0.003659101,0.00004480696,7.424497e-7,0.00005239865,0.00006488761,0.985571,0.003489737,0.00006789928,0.006427736,0.0001481022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1671807,0.00001061944,0.8292271,0.00006071428,0.0007529129,0.0001963039,3.633986e-7,0.0001652786,0.002405911],"genre_scores_gemma":[0.982608,0.00001706284,0.01678263,0.0004572457,0.0001150305,0.00001115574,0.000001717293,0.000002523522,0.00000465563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.983014,"threshold_uncertainty_score":0.6368845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009381940117699838,"score_gpt":0.2143445704307186,"score_spread":0.2049626303130187,"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."}}