{"id":"W2789617153","doi":"10.1016/j.cose.2018.02.018","title":"An efficient intrusion detection in resource-constrained mobile ad-hoc networks","year":2018,"lang":"en","type":"article","venue":"Computers & Security","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Communication source; Node (physics); Intrusion detection system; Mobile ad hoc network; Wireless ad hoc network; Game theory; Resource (disambiguation); Computer network; Scheme (mathematics); Bayesian game; Bayesian probability; Distributed computing; Computer security; Artificial intelligence; Repeated game; Wireless; Network packet","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":[0.0008671579,0.0003061897,0.0003225422,0.000224848,0.0002746003,0.0002631649,0.001405759,0.0002312049,0.0000251477],"category_scores_gemma":[0.00001533767,0.000318177,0.00009650179,0.001082795,0.0002521562,0.0003528072,0.0006349586,0.0005148609,0.00004200153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001979172,"about_ca_system_score_gemma":0.00005306532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001683173,"about_ca_topic_score_gemma":0.0002224432,"domain_scores_codex":[0.9970876,0.000382399,0.0004843331,0.0009856513,0.0003545986,0.0007054184],"domain_scores_gemma":[0.9980138,0.0001651254,0.0001707793,0.001253781,0.0001185106,0.0002779873],"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":[0.00008408886,0.0004168719,0.0001367274,0.00001366387,0.00001340264,0.00005540553,0.004319486,0.1176199,0.0002838916,0.00135119,0.001016747,0.8746887],"study_design_scores_gemma":[0.0006406551,0.0007329021,0.001377898,0.00006321213,0.000004136618,0.00003499701,0.00004724375,0.9799759,0.0004357785,0.0006155839,0.01572811,0.0003435826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3804992,0.0005055474,0.6164769,0.00006921979,0.0013262,0.0004818302,9.371827e-7,0.0004037789,0.0002364275],"genre_scores_gemma":[0.9889427,0.00003370717,0.00978413,0.0005151584,0.0006361188,0.00005317845,0.000007870832,0.00002293888,0.000004143419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8743451,"threshold_uncertainty_score":0.999927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005712860112818473,"score_gpt":0.2268857756541001,"score_spread":0.2211729155412816,"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."}}