{"id":"W4392135566","doi":"10.1016/j.jpdc.2024.104866","title":"HoneyTwin: Securing smart cities with machine learning-enabled SDN edge and cloud-based honeypots","year":2024,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Honeypot; Computer science; Cloud computing; Enhanced Data Rates for GSM Evolution; Edge computing; Computer security; Computer network; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005929676,0.0001931139,0.0003001253,0.0001770575,0.0003599633,0.0006047774,0.0002188331,0.00007402067,0.000006746012],"category_scores_gemma":[0.00004175727,0.000146841,0.000073091,0.000427925,0.00007409117,0.0004252411,0.0001610615,0.000605059,0.000001924456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003859508,"about_ca_system_score_gemma":0.00009244766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002203295,"about_ca_topic_score_gemma":0.00000724623,"domain_scores_codex":[0.9986439,0.0001181099,0.0003992156,0.0002643908,0.0002863298,0.0002880712],"domain_scores_gemma":[0.9991128,0.0002378114,0.0002273619,0.000114651,0.0001309295,0.0001764585],"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":[0.002129769,0.0007190575,0.05634314,0.002773052,0.001572996,0.005221686,0.01409056,0.6243496,0.002489443,0.06579155,0.01577605,0.2087431],"study_design_scores_gemma":[0.001105548,0.0006904462,0.003223822,0.0006021344,0.00003888919,0.0009925099,0.0001068839,0.9813199,0.0002023232,0.001446707,0.01000802,0.0002628068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3719905,0.003955494,0.6225209,0.0007440652,0.0005638665,0.00005508922,0.000003343585,0.00009491961,0.00007191694],"genre_scores_gemma":[0.992953,0.0001468397,0.006275121,0.00009753378,0.0004857281,7.278736e-7,0.000005050279,0.00001059632,0.00002542792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6209625,"threshold_uncertainty_score":0.5988004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009161460302612013,"score_gpt":0.2130426484286773,"score_spread":0.2038811881260653,"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."}}