{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001730429,0.001020066,0.001448868,0.001477897,0.0005782951,0.00146501,0.001431308,0.00128332,0.001502821],"category_scores_gemma":[0.004234245,0.0003226342,0.0008590385,0.0008670824,0.0004960329,0.001262773,0.0006523494,0.001725717,0.0005522707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000702992,"about_ca_system_score_gemma":0.001092014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002654753,"about_ca_topic_score_gemma":0.001889714,"domain_scores_codex":[0.9982948,0.0003513756,0.0002066988,0.0003229518,0.0006125527,0.0002116601],"domain_scores_gemma":[0.9974968,0.00126295,0.0002365118,0.0001251201,0.0008111064,0.00006746451],"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":[0.0002977371,0.0004405807,0.01023057,0.0005254835,0.0002760889,0.0004934261,0.0002196374,0.2948934,0.008202315,0.007084128,0.006913282,0.6704234],"study_design_scores_gemma":[0.000009600484,0.0001484336,0.001353077,0.00003252312,0.0000459494,0.0001754758,0.00004292413,0.9893999,0.003530419,0.002962782,0.002282061,0.00001690572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03991117,0.004654054,0.9453461,0.00123042,0.0003914294,0.0001903814,0.0001320295,0.001989407,0.006154928],"genre_scores_gemma":[0.8262755,0.002964658,0.1620931,0.0006796966,0.0004178123,0.0002782969,0.0004611513,0.0001024313,0.006727322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002654753,"threshold_uncertainty_score":0.009151459,"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."}}