{"id":"W4400689546","doi":"10.1145/3673422.3674901","title":"QUICPro: Integrating Deep Reinforcement Learning to Defend against QUIC Handshake Flooding Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Handshake; Reinforcement learning; Flooding (psychology); Computer science; Computer security; Computer network; Artificial intelligence; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.000389277,0.0002219512,0.0001728363,0.0003481029,0.0002575073,0.0006053727,0.000563424,0.00007053463,0.00005566125],"category_scores_gemma":[0.0001856587,0.0001973041,0.00009442166,0.0008781166,0.00001930964,0.0008455138,0.0005096198,0.0004025583,0.0002200294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002000265,"about_ca_system_score_gemma":0.00004767903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002404098,"about_ca_topic_score_gemma":0.00004886525,"domain_scores_codex":[0.9982526,0.00004062991,0.0003621258,0.0006097149,0.0003173834,0.0004175759],"domain_scores_gemma":[0.9991848,0.0001207603,0.00005291053,0.0004016981,0.00009203993,0.0001477372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008037582,0.00001552418,0.00005413702,0.0001036479,0.00004344742,0.00008469898,0.004561255,0.07570796,0.02697557,0.0717736,0.001291441,0.8193807],"study_design_scores_gemma":[0.00009931783,0.0002828118,0.000006570686,0.0002839387,0.000005282884,0.00002760192,0.0005026896,0.8265504,0.1275707,0.001163893,0.04308272,0.0004239995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003138755,0.0001581248,0.9700782,0.0003206131,0.0004882811,0.0002729662,1.506508e-7,0.003580318,0.02196256],"genre_scores_gemma":[0.7734389,0.00001948856,0.2228523,0.0005791241,0.00009254638,0.00009255821,0.000002370283,0.00002506435,0.002897666],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8189567,"threshold_uncertainty_score":0.8045827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413178302653696,"score_gpt":0.2824288607850944,"score_spread":0.2682970777585574,"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."}}