{"id":"W4226166945","doi":"10.5539/mas.v16n2p12","title":"DDoS Attacks Detection in the IoT Using Deep Gaussian-Bernoulli Restricted Boltzmann Machine","year":2022,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Denial-of-service attack; Computer science; Softmax function; Internet of Things; Deep learning; Bernoulli's principle; Artificial intelligence; Restricted Boltzmann machine; Application layer DDoS attack; Network packet; Trinoo; Machine learning; Computer network; Computer security; The Internet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005621184,0.0003959568,0.0006566003,0.0002797673,0.0002146807,0.0005862967,0.0007502111,0.0006338261,0.0007264149],"category_scores_gemma":[0.001512901,0.0002788964,0.0005669435,0.0003327419,0.0003938268,0.0007808863,0.0006406124,0.0009302417,0.0001746029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006848592,"about_ca_system_score_gemma":0.0007287408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005766402,"about_ca_topic_score_gemma":0.00394308,"domain_scores_codex":[0.9997295,0.00007239421,0.0000193748,0.00007029307,0.00005556801,0.00005284915],"domain_scores_gemma":[0.9996029,0.0002029572,0.00004572387,0.00002832521,0.0000980825,0.0000220075],"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":[0.00009365444,0.00008435079,0.003825897,0.0000404684,0.00004986336,0.00005695818,0.00003187143,0.9529783,0.002469261,0.003261859,0.000638818,0.03646877],"study_design_scores_gemma":[0.0000013763,0.000008672057,0.0001382204,0.000001372097,0.000002270578,0.000005767715,0.000002301621,0.9986598,0.0002694918,0.000858882,0.0000501173,0.000001612509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1758976,0.0007702284,0.8181083,0.0007927614,0.0000996857,0.00005523268,0.0001695063,0.0008432867,0.003263451],"genre_scores_gemma":[0.9707864,0.0002337905,0.02673497,0.000132503,0.00001916702,0.00005291378,0.0001479981,0.00001745114,0.001874744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005766402,"threshold_uncertainty_score":0.01146567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878964373506115,"score_gpt":0.2470599892157391,"score_spread":0.228270345480678,"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."}}