{"id":"W1521458057","doi":"10.1007/978-0-387-34827-8_5","title":"A Sliding Window Based Management Traffic Clustering Algorithm for 802.11 WLAN Intrusion Detection","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cluster analysis; Sliding window protocol; Window (computing); Data mining; Computer science; Intrusion detection system; Algorithm; Intrusion; Sample (material); Variance (accounting); Robustness (evolution); Real-time computing; Pattern recognition (psychology); Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007201533,0.0004890286,0.0003929542,0.0007629989,0.000460791,0.0002776517,0.0006590137,0.0005061061,0.0001442855],"category_scores_gemma":[0.000005573627,0.0004981128,0.0003249607,0.0001700934,0.00002897092,0.000355212,0.0003702618,0.0003906526,0.00005934887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003059882,"about_ca_system_score_gemma":0.00002882961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001751219,"about_ca_topic_score_gemma":0.0003186599,"domain_scores_codex":[0.9973962,0.0000257113,0.0005863251,0.000946153,0.0005381888,0.0005074189],"domain_scores_gemma":[0.9986333,0.000122269,0.0002997002,0.000659935,0.0001289014,0.0001558624],"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":[0.0000260907,0.00002033223,6.264938e-8,0.00007857344,0.00003649226,0.0000225978,0.0000614803,0.0008230399,0.00005342359,0.01156488,0.0002009372,0.9871121],"study_design_scores_gemma":[0.0007232552,0.0002904751,0.000003576306,0.000371035,0.00004710781,0.00002987482,0.00001198797,0.8796359,0.0007381268,0.001724964,0.115842,0.0005816831],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003296128,0.0001181625,0.9528252,0.00008296254,0.002312678,0.001095764,0.00000456729,0.000641452,0.04288623],"genre_scores_gemma":[0.0214114,0.0005079096,0.8265349,0.002278458,0.003337343,0.0002391506,0.00009813744,0.0002884119,0.1453043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9865304,"threshold_uncertainty_score":0.999747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02486337887969635,"score_gpt":0.2375060649928891,"score_spread":0.2126426861131928,"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."}}