{"id":"W4382678370","doi":"10.1016/j.iot.2023.100861","title":"ARP-PROBE: An ARP spoofing detector for Internet of Things networks using explainable deep learning","year":2023,"lang":"en","type":"article","venue":"Internet of Things","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"Zayed University; United Arab Emirates University","keywords":"Spoofing attack; ARP spoofing; Computer science; Address Resolution Protocol; Network packet; Deep learning; Botnet; Internet of Things; Artificial intelligence; Computer network; Feature (linguistics); The Internet; Feature extraction; Computer security; Machine learning; Data mining; Real-time computing; Internet Protocol; World Wide Web","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"],"consensus_categories":[],"category_scores_codex":[0.001384097,0.0002615151,0.0004418191,0.0003784317,0.0001333392,0.0002013917,0.00128788,0.0002162893,0.00004254088],"category_scores_gemma":[0.0002410509,0.0002671094,0.0002208455,0.0007027303,0.00009099411,0.002060827,0.0007264895,0.0004923486,0.00001031339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009276337,"about_ca_system_score_gemma":0.00003058707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001108609,"about_ca_topic_score_gemma":0.00002139584,"domain_scores_codex":[0.997655,0.0001387364,0.0006964587,0.00058682,0.0003698093,0.0005531746],"domain_scores_gemma":[0.9983131,0.0002898004,0.0006059908,0.0004288185,0.0002451091,0.0001172035],"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":[0.001162505,0.0004797719,0.002612557,0.001479462,0.0005489833,0.0001002267,0.1317094,0.1084466,0.07925225,0.07072804,0.002271882,0.6012083],"study_design_scores_gemma":[0.0003526331,0.0005650617,0.0000726551,0.0004066252,0.00001935279,0.00002413331,0.0002235081,0.9564641,0.03645486,0.003072124,0.002086167,0.0002588363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3411691,0.0001139758,0.6572196,0.00004239533,0.000826272,0.0002117825,2.729297e-7,0.0002671252,0.0001493923],"genre_scores_gemma":[0.9533983,0.00002676827,0.04549597,0.0001959194,0.0001604845,0.00002022742,0.000007833557,0.00003960028,0.0006548605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8480175,"threshold_uncertainty_score":0.9999781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226591071170108,"score_gpt":0.2560856048699473,"score_spread":0.2334264977529365,"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."}}