{"id":"W3146884116","doi":"10.18280/ria.350102","title":"Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Engineering Research Board; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Computer science; Machine learning; Artificial intelligence; Feature (linguistics); Random forest; Denial-of-service attack; Decision tree; Set (abstract data type); Support vector machine; Internet of Things; Intrusion detection system; Data mining; The Internet; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004799293,0.0001555904,0.0002002166,0.0001351929,0.0003016689,0.0001960962,0.0002324523,0.0001244564,0.00004935883],"category_scores_gemma":[0.00007420149,0.0001725004,0.00006107842,0.001079879,0.00005805035,0.0002945975,0.0002298657,0.0004291142,0.0000183966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005813179,"about_ca_system_score_gemma":0.00002755032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001099642,"about_ca_topic_score_gemma":0.0003814743,"domain_scores_codex":[0.9984244,0.0002080674,0.0003677079,0.0005481134,0.0001241647,0.0003275803],"domain_scores_gemma":[0.9993092,0.000121077,0.00008481213,0.0003401199,0.00006289379,0.00008188747],"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":[0.00001277614,0.00007187088,0.001441738,0.0000284599,0.00000700632,0.0000343575,0.0009733945,0.7674986,0.004040707,0.001000709,0.000003272521,0.2248871],"study_design_scores_gemma":[0.00004925862,0.0000632591,0.0002073967,0.00005888291,0.000004409248,0.0001229638,0.0002345218,0.959392,0.03840313,0.0003137669,0.0009656263,0.0001848321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3829241,0.001318036,0.6150115,0.0001508911,0.0002162356,0.0000889604,1.797601e-7,0.0000642885,0.0002257415],"genre_scores_gemma":[0.9939333,0.0002759453,0.00541356,0.00007931008,0.0001066419,0.000007210096,0.000002434048,0.00001228967,0.0001693719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6110091,"threshold_uncertainty_score":0.7034363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07656661300283525,"score_gpt":0.257093671594537,"score_spread":0.1805270585917018,"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."}}