{"id":"W4396557969","doi":"10.1002/spe.3338","title":"Dynamic hierarchical intrusion detection task offloading in IoT edge networks","year":2024,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Inference; Edge device; Exploit; Cloud computing; Enhanced Data Rates for GSM Evolution; Intrusion detection system; Edge computing; Task (project management); Inference engine; Distributed computing; Range (aeronautics); Data mining; Artificial intelligence; Computer security","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":[0.0005102981,0.0001644526,0.0001422436,0.000210627,0.0002824728,0.0005037024,0.0002969881,0.000152348,0.00001586831],"category_scores_gemma":[0.0004938399,0.0001572691,0.00004129173,0.001009846,0.00009036758,0.002022229,0.0003219178,0.0006615955,0.00002858199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032843,"about_ca_system_score_gemma":0.00004460203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001032537,"about_ca_topic_score_gemma":0.00008349182,"domain_scores_codex":[0.9983528,0.0001639121,0.0002728132,0.0006205741,0.0002463135,0.0003435399],"domain_scores_gemma":[0.9988619,0.0006296621,0.00006076948,0.0002890036,0.00004594398,0.0001126934],"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":[0.00006305602,0.0000519696,0.00007675171,0.00003319834,0.000008016932,0.0001337681,0.009299562,0.0005926862,0.001932295,0.002555062,0.00007158523,0.985182],"study_design_scores_gemma":[0.0001723105,0.0002031605,0.0005077273,0.0002051469,0.000008335611,0.000439607,0.0005061406,0.9498317,0.0006628576,0.0025524,0.04459422,0.0003164033],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1934095,0.003432633,0.800004,0.0007943557,0.001671384,0.0001460552,4.159231e-7,0.0004063493,0.0001352614],"genre_scores_gemma":[0.9847447,0.001182928,0.01334306,0.0004968847,0.0001255262,0.00004704274,0.000001202698,0.00001223687,0.00004642637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9848657,"threshold_uncertainty_score":0.6413246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007705413725137718,"score_gpt":0.2663588571658311,"score_spread":0.2586534434406934,"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."}}