{"id":"W4404583394","doi":"10.1515/jisys-2023-0150","title":"Enhancing IoT device security: CNN-SVM hybrid approach for real-time detection of DoS and DDoS attacks","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Systems","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Zarqa University","keywords":"Denial-of-service attack; Computer science; Support vector machine; Internet of Things; Artificial intelligence; Application layer DDoS attack; Machine learning; Computer security; Pattern recognition (psychology); Data mining; The Internet; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000585095,0.0009922595,0.000570503,0.0007989796,0.0002023946,0.0005505379,0.0007959104,0.0007428182,0.001096176],"category_scores_gemma":[0.001089354,0.0002543884,0.0005840365,0.0003914631,0.0001815037,0.0007131351,0.0003949412,0.0006799666,0.0003870787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006209751,"about_ca_system_score_gemma":0.0005141885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006717833,"about_ca_topic_score_gemma":0.006218342,"domain_scores_codex":[0.9997264,0.00003648157,0.00001786056,0.00007682467,0.00007365151,0.00006885264],"domain_scores_gemma":[0.9996525,0.00009629896,0.00004186354,0.00003289331,0.0001526711,0.00002387006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005892062,0.0005907438,0.01853701,0.0001420025,0.0001961689,0.0003423473,0.00007344796,0.3894049,0.02576288,0.001346703,0.008094002,0.5549206],"study_design_scores_gemma":[0.000003074255,0.00003575139,0.0007227771,0.000003697419,0.00001028043,0.00001621988,0.000007461068,0.9966477,0.002149274,0.0001596935,0.0002409056,0.00000318457],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5103042,0.002827106,0.472068,0.001198279,0.0006181925,0.0001920109,0.0006477687,0.00510982,0.007034544],"genre_scores_gemma":[0.9626898,0.0002474459,0.03316356,0.0001565639,0.00005348724,0.00003530702,0.0004526879,0.000035737,0.00316552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006717833,"threshold_uncertainty_score":0.01335746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657984515503556,"score_gpt":0.2585408536382038,"score_spread":0.2419610084831682,"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."}}