{"id":"W2950965276","doi":"10.1016/j.comcom.2019.06.004","title":"Optimizing the network diversity to improve the resilience of networks against unknown attacks","year":2019,"lang":"en","type":"article","venue":"Computer Communications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Army Research Office; National Institute of Standards and Technology; Natural Sciences and Engineering Research Council of Canada; Georgian National Science Foundation; Office of Naval Research; Concordia University; National Science Foundation","keywords":"Computer science; Resilience (materials science); Heuristic; Network security; Diversification (marketing strategy); Diversity (politics); Optimization problem; Computer security; Metric (unit); Distributed computing; Risk analysis (engineering); Artificial intelligence; Algorithm; Business","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006497517,0.0004462494,0.0004773833,0.0004884939,0.0004412136,0.0006345928,0.0005347013,0.0004693546,0.001206864],"category_scores_gemma":[0.003801772,0.0001608636,0.0001779794,0.000360593,0.000434269,0.001293382,0.0009493234,0.0006049466,0.0002249476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005761931,"about_ca_system_score_gemma":0.0005490763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006026949,"about_ca_topic_score_gemma":0.00107591,"domain_scores_codex":[0.9994916,0.0001444259,0.00001569702,0.0001080555,0.0001055333,0.0001346896],"domain_scores_gemma":[0.9984739,0.0007472569,0.0001794509,0.0002286493,0.000265036,0.0001055963],"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":[0.0003715883,0.00017919,0.003514263,0.00007519599,0.00008807498,0.00009565392,0.00009001361,0.7975024,0.09269034,0.01357649,0.001419451,0.09039731],"study_design_scores_gemma":[0.0000268204,0.0001790456,0.0008076256,0.000008751482,0.00002990169,0.00009160971,0.00004414963,0.9754921,0.01363581,0.008765619,0.0009061858,0.00001243923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5182227,0.001411663,0.4698056,0.0008395015,0.000141564,0.00004113816,0.0001019615,0.0006217151,0.00881413],"genre_scores_gemma":[0.986629,0.000132522,0.01259846,0.00004066794,0.0000333473,0.000007415978,0.00002058319,0.00001979326,0.0005181473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001206864,"threshold_uncertainty_score":0.00418061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01733865855328208,"score_gpt":0.2396521749782382,"score_spread":0.2223135164249561,"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."}}