{"id":"W4309647886","doi":"10.1145/3565386.3565493","title":"Deep learning driven anomaly based intrusion detection system for IoT","year":2022,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Hyperparameter; Anomaly detection; Artificial intelligence; Intrusion detection system; Oversampling; Particle swarm optimization; Machine learning; Deep learning; Feature selection; Anomaly (physics); Meta learning (computer science); Feature (linguistics); Data mining; Engineering","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":[0.0005422102,0.0005778827,0.0007222793,0.0006616962,0.0003375534,0.0006060021,0.001239907,0.0005027197,0.001166607],"category_scores_gemma":[0.0008430688,0.0002436799,0.0003827676,0.0004113986,0.0002675466,0.001156854,0.0008678689,0.0009029094,0.0003270476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007452339,"about_ca_system_score_gemma":0.0006429177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001648451,"about_ca_topic_score_gemma":0.002869065,"domain_scores_codex":[0.9996544,0.00003976395,0.00002799708,0.0000797472,0.0001414685,0.00005659866],"domain_scores_gemma":[0.9995506,0.00009369664,0.00007325948,0.00006651764,0.0001813959,0.0000345303],"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":[0.0007353821,0.0009254796,0.01467643,0.0001980271,0.0002939786,0.0005467416,0.0001578876,0.2797152,0.09560592,0.01039596,0.009882313,0.5868667],"study_design_scores_gemma":[0.00000767926,0.0000741546,0.0007356507,0.000004722005,0.00001616658,0.00007269827,0.000007349914,0.9858869,0.01046777,0.001887928,0.0008296521,0.000009381491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09691244,0.0003136529,0.8898528,0.0004270003,0.0001563803,0.0001272014,0.0002953679,0.009212743,0.002702515],"genre_scores_gemma":[0.87887,0.0001556681,0.1176366,0.0002333276,0.00004558995,0.0001262946,0.0004176311,0.00007428445,0.002440663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001648451,"threshold_uncertainty_score":0.005407095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009029359710324324,"score_gpt":0.205769935004624,"score_spread":0.1967405752942997,"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."}}