{"id":"W4367323040","doi":"10.18280/mmep.100234","title":"Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bottleneck; Computer science; Internet of Things; Artificial neural network; Artificial intelligence; Spike (software development); Preprocessor; Traffic classification; Energy (signal processing); Machine learning; Support vector machine; Point (geometry); Real-time computing; Data mining; Computer network; Embedded system; Network packet","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003495527,0.0004660009,0.0003855713,0.0008828263,0.0002277769,0.0006996841,0.0006087274,0.0005094009,0.000674381],"category_scores_gemma":[0.001033991,0.0001539063,0.0005095442,0.0006517441,0.0002409439,0.0008735966,0.0003127738,0.0005723927,0.0001555163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007626234,"about_ca_system_score_gemma":0.0003998726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004270334,"about_ca_topic_score_gemma":0.003053395,"domain_scores_codex":[0.9998442,0.00002796316,0.0000117862,0.00003724447,0.00004886844,0.00002995524],"domain_scores_gemma":[0.9996942,0.000104316,0.00004245884,0.00002151036,0.0001231019,0.00001443548],"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":[0.0001761607,0.0001840198,0.01488941,0.00006282336,0.00007681109,0.0001442817,0.00005465981,0.8056939,0.006924516,0.005688046,0.001622172,0.1644832],"study_design_scores_gemma":[5.908482e-7,0.000005810639,0.0002696355,9.868967e-7,0.000001733737,0.000005182348,0.000003130252,0.9987006,0.0003513698,0.00060975,0.0000498407,0.000001313155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3102774,0.0003452504,0.6833447,0.0004293037,0.0001301726,0.0000609101,0.0002706841,0.0008817674,0.004259882],"genre_scores_gemma":[0.9769797,0.0001544352,0.02069178,0.00004461397,0.00003285442,0.00003456373,0.0003371161,0.00001803133,0.001706768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004270334,"threshold_uncertainty_score":0.00849098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0350037431706921,"score_gpt":0.2220549675835737,"score_spread":0.1870512244128816,"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."}}