{"id":"W2884826434","doi":"10.1109/dsn.2018.00064","title":"Inferring, Characterizing, and Investigating Internet-Scale Malicious IoT Device Activities: A Network Telescope Perspective","year":2018,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"JST-Mirai Program; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Botnet; Malware; Computer science; Internet of Things; Computer security; Denial-of-service attack; The Internet; Perspective (graphical); Scale (ratio); Malware analysis; Data science; Internet privacy; World Wide Web; Artificial intelligence","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":[0.00104374,0.0005723488,0.0003446309,0.002617293,0.0004493285,0.001185219,0.0007774752,0.0006662069,0.0004588851],"category_scores_gemma":[0.006653638,0.0002894593,0.0003211765,0.001110854,0.0009809331,0.002925657,0.00108227,0.0009911432,0.0002466988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006210699,"about_ca_system_score_gemma":0.0003791007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003024308,"about_ca_topic_score_gemma":0.005165969,"domain_scores_codex":[0.9993468,0.0002395786,0.00003041325,0.0001630413,0.0001586309,0.00006149543],"domain_scores_gemma":[0.9966648,0.001726376,0.0006161358,0.0006697824,0.0002344814,0.00008835746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001472421,0.0005716673,0.6019007,0.0002828186,0.0002516141,0.0007101391,0.001562588,0.2301673,0.02042527,0.02490991,0.002946984,0.1161237],"study_design_scores_gemma":[0.000006818767,0.00009871194,0.08834496,0.00006385014,0.00004624008,0.0007208403,0.00138065,0.8725601,0.008069508,0.02510034,0.00357331,0.00003464048],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8290652,0.0006063577,0.1616772,0.001109588,0.00003475956,0.000153362,0.001235621,0.000568504,0.005549326],"genre_scores_gemma":[0.9765172,0.0002676357,0.02213637,0.00007489122,0.00002737836,0.00003516353,0.0006457274,0.0000264488,0.0002691251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003024308,"threshold_uncertainty_score":0.006013393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607333226073757,"score_gpt":0.2514360364275527,"score_spread":0.2353627041668151,"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."}}