{"id":"W3212714379","doi":"10.32920/ryerson.14654013.v1","title":"Preventing Collaborative Blackhole Attacks on Mobile Ad Hoc Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Packet drop attack; Computer network; Network packet; Computer science; Node (physics); Routing protocol; Overhead (engineering); Benchmark (surveying); Routing (electronic design automation); DSRFLOW; Throughput; Dynamic Source Routing; End-to-end delay; Protocol (science); Mobile ad hoc network; Wireless ad hoc network; Link-state routing protocol; Engineering; Wireless; Telecommunications; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009711185,0.0007598411,0.0006262888,0.001007869,0.0005400814,0.001054352,0.0007137147,0.0008734983,0.0006980801],"category_scores_gemma":[0.005348794,0.0002719759,0.0003115201,0.0004250625,0.0005789282,0.002149751,0.001712439,0.0006054778,0.0002613869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031535,"about_ca_system_score_gemma":0.0003843952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005169691,"about_ca_topic_score_gemma":0.0004638152,"domain_scores_codex":[0.9989308,0.00032029,0.00004898886,0.0001240862,0.0004299089,0.0001460065],"domain_scores_gemma":[0.9973927,0.001317393,0.0003442532,0.000495658,0.0003583339,0.00009169711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005743402,0.0003658323,0.002910624,0.0004320746,0.000188998,0.0005844455,0.0003855199,0.49174,0.07979708,0.04826535,0.006896945,0.3678588],"study_design_scores_gemma":[0.0000433063,0.0003783068,0.0005668144,0.00002557887,0.00004035609,0.0003129869,0.00009973483,0.9647717,0.01618548,0.01309389,0.004458251,0.00002353584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2332776,0.002397384,0.7481649,0.0006424678,0.0003068566,0.0002482017,0.00004953332,0.002234189,0.01267894],"genre_scores_gemma":[0.9636266,0.0006558918,0.03358146,0.00006150627,0.00008653903,0.00007220423,0.00004309119,0.00002851192,0.001844171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001054352,"threshold_uncertainty_score":0.005135834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286347918051939,"score_gpt":0.2681310451644704,"score_spread":0.2552675659839511,"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."}}