{"id":"W4210493470","doi":"10.1109/icoin53446.2022.9687165","title":"TMorph: A Traffic Morphing Framework to Test Network Defenses Against Adversarial Attacks","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Information Networking (ICOIN)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Computer security; Obfuscation; Malware; Encryption; Traffic shaping; Morphing; Adversarial system; Evasion (ethics); Computer network; Botnet; The Internet; Network traffic control; Network packet; Operating system; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009928771,0.0003421152,0.0002887707,0.0004630343,0.001281208,0.000964791,0.001903719,0.0001331565,0.001376285],"category_scores_gemma":[0.0002776207,0.0003903538,0.0001797987,0.001314278,0.00003736006,0.00182036,0.001109951,0.001133931,0.0003596511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000476794,"about_ca_system_score_gemma":0.0002379102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001687496,"about_ca_topic_score_gemma":0.00001513212,"domain_scores_codex":[0.996383,0.000193755,0.0008600715,0.0004930208,0.001449446,0.0006207655],"domain_scores_gemma":[0.997858,0.0004480774,0.0005379245,0.0005872249,0.0003120438,0.0002567082],"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":[0.0001495632,0.0001090833,0.0001056965,0.000006578577,0.00005141915,0.00001333238,0.001847755,0.7464102,0.00001796838,0.1376883,0.04418597,0.06941409],"study_design_scores_gemma":[0.000410711,0.0003932346,0.0001293153,0.000116785,0.000006828553,0.00002867914,0.0002029023,0.7222759,0.00001213272,0.003445887,0.2725703,0.0004073679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09537192,0.0001777465,0.7573966,0.01889537,0.05564861,0.002200113,0.0001806886,0.002132195,0.06799674],"genre_scores_gemma":[0.9795381,0.0001071038,0.006407213,0.01121114,0.002164813,0.0002394037,0.0001476691,0.00002122888,0.0001633359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8841662,"threshold_uncertainty_score":0.9998549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02422612287375134,"score_gpt":0.2533472394083598,"score_spread":0.2291211165346085,"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."}}