{"id":"W4304782139","doi":"10.3390/s22207726","title":"Towards Developing a Robust Intrusion Detection Model Using Hadoop–Spark and Data Augmentation for IoT Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Cistel Technology (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SPARK (programming language); Computer science; Robustness (evolution); Intrusion detection system; Big data; Data mining; Machine learning; Anomaly detection; Artificial intelligence; Key (lock); Internet of Things","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006133483,0.0001320158,0.0001329038,0.0001351027,0.001045278,0.0001449266,0.0004310833,0.00006031014,0.000006162521],"category_scores_gemma":[0.00003514426,0.0001465982,0.00002960089,0.0004531771,0.00002469582,0.0003721351,0.000928483,0.0001997308,5.407991e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001841081,"about_ca_system_score_gemma":0.00008414537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001168487,"about_ca_topic_score_gemma":0.0000790935,"domain_scores_codex":[0.9986055,0.0001141833,0.0002405974,0.0005409399,0.0002446912,0.0002541099],"domain_scores_gemma":[0.999254,0.00005605756,0.0001375108,0.0004372428,0.00005974318,0.00005539816],"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":[0.000043494,0.0000111813,0.000005562807,0.00001080981,0.000009105433,0.000001170939,0.0003776217,0.9004335,0.0006122395,0.001290908,0.000155916,0.09704842],"study_design_scores_gemma":[0.0003325414,0.00007672129,0.00004131804,0.00001402036,0.00001336328,0.00004497295,0.0001121637,0.993261,0.0008164115,0.003207497,0.001891879,0.0001881246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2707424,0.00007696265,0.7278001,0.0004362224,0.0005675341,0.0002597859,0.000006096577,0.00009571797,0.00001512963],"genre_scores_gemma":[0.7703399,0.00005687452,0.2287599,0.0005248613,0.0002013937,0.00003010677,0.00003438648,0.00001882651,0.00003378086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4995975,"threshold_uncertainty_score":0.8039538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07873728330986736,"score_gpt":0.2934666326543262,"score_spread":0.2147293493444589,"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."}}