{"id":"W4399980204","doi":"10.18280/ijsse.140317","title":"Design of an Efficient Forensic Layer for IoT Network Traffic Analysis Engine using Deep Packet Inspection via Recurrent Neural Networks","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Deep packet inspection; Network packet; Computer network; Internet of Things; Layer (electronics); Artificial neural network; Application layer; Real-time computing; Embedded system; Artificial intelligence; Software; Operating system","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":[0.0009781421,0.0001811812,0.0003757984,0.0005024334,0.00006302603,0.000169727,0.0003999834,0.00008011167,0.000004212291],"category_scores_gemma":[0.00003489573,0.0001630517,0.0003216454,0.0005841031,0.00002312755,0.0002766734,0.00006298104,0.0002972507,1.156809e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001154361,"about_ca_system_score_gemma":0.00003587128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003319737,"about_ca_topic_score_gemma":0.00001130456,"domain_scores_codex":[0.9983746,0.00005597908,0.0007215035,0.0002411892,0.0003738304,0.0002328576],"domain_scores_gemma":[0.9989728,0.0002469447,0.0002381235,0.00008822866,0.0003537294,0.0001001544],"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":[0.00007540672,0.00004439057,0.00000827346,0.00002110293,0.0009423242,0.00002836379,0.0009610648,0.9756265,0.00004069014,0.005125239,0.000004871944,0.01712177],"study_design_scores_gemma":[0.0002591322,0.0001539495,0.00009258919,0.0001291371,0.0002506425,0.000141328,0.00002807814,0.9986771,0.00004434475,0.00002506854,0.00004750888,0.0001511215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1770285,0.001276253,0.8199772,0.00007945263,0.00151469,0.00007545856,0.000003044941,0.00004454123,8.847759e-7],"genre_scores_gemma":[0.9647254,0.00006964778,0.03447395,0.00001602746,0.0006918309,0.00000120466,0.000008062044,0.00001315627,7.301284e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7876969,"threshold_uncertainty_score":0.6649056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212013124157628,"score_gpt":0.2440556490095684,"score_spread":0.2319355177679921,"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."}}