{"id":"W2097324407","doi":"10.1109/icc.2007.196","title":"Enhanced Intrusion Detection System for Discovering Malicious Nodes in Mobile Ad Hoc Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer network; Computer science; Intrusion detection system; Wireless ad hoc network; Mobile ad hoc network; Node (physics); Network partition; Vehicular ad hoc network; Throughput; Routing protocol; Computer security; Routing (electronic design automation); Wireless; Network packet; Engineering; Telecommunications","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":[0.0007756594,0.0001753019,0.0002191646,0.0001302482,0.0001190034,0.0001262603,0.000486555,0.0001430585,0.000004143026],"category_scores_gemma":[0.00001282123,0.0001604377,0.00007312681,0.0005203059,0.00001921676,0.0004561604,0.000230496,0.0001628294,0.000008800702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002795793,"about_ca_system_score_gemma":0.00001651906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002740222,"about_ca_topic_score_gemma":0.001301703,"domain_scores_codex":[0.998338,0.00003664169,0.0004097793,0.0004920063,0.0001780916,0.0005454129],"domain_scores_gemma":[0.9990367,0.0002197696,0.0001139409,0.0004909427,0.00005188134,0.00008670917],"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":[0.00007786671,0.00004256754,0.00007497844,0.00005373773,0.000007722828,0.00001094593,0.0003608019,0.09329348,0.01503345,0.002351115,0.00001937803,0.888674],"study_design_scores_gemma":[0.0007547141,0.0002509775,0.0008278709,0.0001463084,0.000004654754,0.00001929286,0.00027766,0.9100426,0.08589312,0.0001121494,0.00134421,0.0003264273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1823722,0.0007319286,0.8144305,0.00000793211,0.0008950479,0.0007091064,3.235251e-7,0.0002825618,0.0005703968],"genre_scores_gemma":[0.9805522,0.00008844738,0.01871184,0.0000616313,0.0001945292,0.0002494882,0.000001863596,0.00001930854,0.0001206763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8883475,"threshold_uncertainty_score":0.654246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006723387109422721,"score_gpt":0.2325099173924055,"score_spread":0.2257865302829827,"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."}}