{"id":"W4402067405","doi":"10.18280/ijsse.140401","title":"Enhancing Mobile Ad Hoc Network Security: An Anomaly Detection Approach Using Support Vector Machine for Black-Hole Attack Detection","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; Mobile ad hoc network; Packet drop attack; Anomaly detection; Black hole (networking); Computer security; Wireless ad hoc network; Artificial intelligence; Telecommunications; Wireless; Routing protocol","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":[0.0007072779,0.0006938156,0.0008068701,0.001141192,0.0003151267,0.0006104207,0.0007443147,0.0006726256,0.000447726],"category_scores_gemma":[0.001722191,0.0001840424,0.0004687917,0.0007264028,0.0003205448,0.001367548,0.0005180637,0.0009203228,0.0001936846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002615958,"about_ca_system_score_gemma":0.0004219406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001281824,"about_ca_topic_score_gemma":0.001079288,"domain_scores_codex":[0.9993184,0.000125921,0.00004281772,0.0001049454,0.0003073376,0.0001007091],"domain_scores_gemma":[0.9990539,0.0002894637,0.0001156333,0.00008614878,0.0004124297,0.00004239783],"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":[0.0003837564,0.0005485077,0.01023249,0.0001339054,0.0001973797,0.0002929628,0.0001076406,0.1558687,0.06603576,0.003661719,0.00181115,0.760726],"study_design_scores_gemma":[0.000005232833,0.0001314312,0.0009920479,0.000003255368,0.00002065002,0.00008711056,0.00001742718,0.9893154,0.008085193,0.0009575955,0.0003763753,0.000008345544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1106947,0.0003856392,0.8865264,0.0001887324,0.0001225211,0.00005684768,0.00003445718,0.001190596,0.0008000276],"genre_scores_gemma":[0.8800035,0.0002310769,0.1185297,0.00004555715,0.00005333623,0.00002232053,0.00006517016,0.00002426158,0.001025229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001281824,"threshold_uncertainty_score":0.003740489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009902590369529833,"score_gpt":0.2455864303511187,"score_spread":0.2356838399815888,"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."}}