{"id":"W4414015728","doi":"10.11159/cist25.128","title":"Identifying Malicious Network Traffic Detection using Graph Transformers & Masked Autoencoders","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Transformer; Computer network; Artificial intelligence; Pattern recognition (psychology); Computer security; Engineering; Electrical engineering; Voltage","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":[0.0006686408,0.001019645,0.0004279288,0.001209387,0.0002289906,0.000574594,0.0008422867,0.0007190922,0.0009796134],"category_scores_gemma":[0.002474437,0.0003084904,0.0005656915,0.0006146465,0.0006102764,0.0013021,0.0006838252,0.0008693089,0.0003529754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006058092,"about_ca_system_score_gemma":0.0005402736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005579058,"about_ca_topic_score_gemma":0.007430665,"domain_scores_codex":[0.9996706,0.00007918957,0.00001407968,0.0001225551,0.00007434724,0.00003925941],"domain_scores_gemma":[0.9991042,0.0004187317,0.0001212276,0.0001556976,0.0001644306,0.00003558744],"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":[0.000358032,0.0002691936,0.00612178,0.0001094242,0.000139152,0.0002010896,0.0001087834,0.6206812,0.03281137,0.006735143,0.002866234,0.3295985],"study_design_scores_gemma":[0.00000265176,0.00002249446,0.0004933052,0.000002124909,0.000005297498,0.00002212155,0.000006088318,0.9945745,0.003274144,0.001455179,0.0001391368,0.000002965481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1349536,0.0002358698,0.860988,0.0001895712,0.00004387844,0.00006645208,0.000279304,0.001974533,0.001268894],"genre_scores_gemma":[0.8467523,0.0001593771,0.1507881,0.0001010308,0.00002810781,0.0000414068,0.0006364452,0.00008957005,0.001403736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005579058,"threshold_uncertainty_score":0.01109314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009352221138913602,"score_gpt":0.2185539963362211,"score_spread":0.2092017751973075,"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."}}