{"id":"W4206360774","doi":"10.1109/mcom.009.2100389","title":"ATMoS+: Generalizable Threat Mitigation in SDN Using Permutation Equivariant and Invariant Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reinforcement learning; Scalability; Invariant (physics); Distributed computing; Equivariant map; Software-defined networking; Set (abstract data type); Software; Network architecture; Permutation (music); Artificial intelligence; Theoretical computer science; Computer network; Operating system","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":[0.0003996356,0.0001424931,0.0001807535,0.00009897667,0.0003676467,0.0002380441,0.0005839448,0.00007457558,0.00002017661],"category_scores_gemma":[0.0001080646,0.0001551192,0.00003662527,0.0007747987,0.00006387681,0.0005728889,0.0005592525,0.0002462796,0.00002009853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001275889,"about_ca_system_score_gemma":0.0001235465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001545806,"about_ca_topic_score_gemma":0.000336082,"domain_scores_codex":[0.9986097,0.0002669011,0.0003858215,0.0003051638,0.0001803115,0.0002520985],"domain_scores_gemma":[0.9981576,0.0002100471,0.0001415786,0.00123691,0.0001812373,0.00007268861],"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":[0.000008775649,0.0001551173,0.007119853,0.00003787354,0.00004227186,0.00004062674,0.002698138,0.8674068,0.01356556,0.09587735,0.0002862583,0.01276135],"study_design_scores_gemma":[0.0004946007,0.00002977225,0.003683991,0.00008061722,0.00001458703,0.00006497383,0.00005559085,0.9904394,0.0003929748,0.002989132,0.001572344,0.0001819779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03024391,0.002406551,0.9633497,0.001942032,0.0001859604,0.0001908904,6.398515e-7,0.000114334,0.001565965],"genre_scores_gemma":[0.8030521,0.001242658,0.194912,0.0003273936,0.00004776387,0.00003498161,0.00007968101,0.00001504216,0.0002883821],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7728082,"threshold_uncertainty_score":0.6325577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04306211764270063,"score_gpt":0.2847231214594093,"score_spread":0.2416610038167087,"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."}}