{"id":"W2948694540","doi":"10.1007/978-3-030-22038-9_5","title":"Detecting, Fingerprinting and Tracking Reconnaissance Campaigns Targeting Industrial Control Systems","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec; Thales (Canada); Concordia University","funders":"","keywords":"Computer science; Network packet; Botnet; Fingerprint (computing); Computer security; Computer network; Ip address; Ethernet; The Internet; Internet Protocol; Real-time computing; 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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002520481,0.0005342638,0.0007019917,0.0006352013,0.0005704752,0.00135571,0.001643637,0.0005741366,0.00001246178],"category_scores_gemma":[0.0004123365,0.0005257848,0.0001135428,0.0005065221,0.0003620773,0.0009674668,0.00081444,0.00172008,0.00002062048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002454452,"about_ca_system_score_gemma":0.0003456582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005204478,"about_ca_topic_score_gemma":0.00003971617,"domain_scores_codex":[0.995914,0.0001093074,0.0007734147,0.001675539,0.0007473087,0.0007804726],"domain_scores_gemma":[0.9970174,0.001049249,0.0006937858,0.0008291736,0.0002443405,0.0001660687],"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":[0.00001550423,0.00001154859,0.0003257957,0.00008301887,0.00001908338,0.00003867976,0.001013898,0.07426249,0.0005286679,0.004857255,0.00002349807,0.9188206],"study_design_scores_gemma":[0.0006402566,0.000226021,0.00003558379,0.001405803,0.00001174266,0.0001308893,0.00000121434,0.984615,0.0009809432,0.007497014,0.003639264,0.0008162851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00215329,0.001322467,0.9877709,0.0001566143,0.006576221,0.0006591964,0.000002487306,0.0002182189,0.001140649],"genre_scores_gemma":[0.9702674,0.00005696996,0.02707564,0.0004622175,0.001933399,0.000009067067,0.00000122842,0.00004261782,0.0001514467],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9681141,"threshold_uncertainty_score":0.9997194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02671016277980487,"score_gpt":0.2304512463068086,"score_spread":0.2037410835270037,"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."}}