{"id":"W2896437058","doi":"10.1109/pst.2017.00019","title":"Context Sensitive and Secure Parser Generation for Deep Packet Inspection of Binary Protocols","year":2017,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Network packet; Parsing; Protocol (science); Context (archaeology); Deep packet inspection; Process (computing); Intrusion detection system; Computer network; Binary number; Computer security; Artificial intelligence; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001800428,0.0007087046,0.0005589533,0.000945401,0.0005505256,0.001167982,0.001408801,0.001050032,0.002208235],"category_scores_gemma":[0.007160021,0.000673486,0.0005758547,0.0005438858,0.001106341,0.001981436,0.001757867,0.001380754,0.0008921871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006356839,"about_ca_system_score_gemma":0.001201712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00100169,"about_ca_topic_score_gemma":0.00140046,"domain_scores_codex":[0.9981053,0.0005699533,0.0001493414,0.0003776132,0.0006200571,0.0001777312],"domain_scores_gemma":[0.9945845,0.00294083,0.0004288366,0.001405786,0.0005452698,0.00009481153],"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":[0.001073483,0.0003658511,0.007845595,0.0003835578,0.0001144463,0.001480115,0.0011181,0.07188306,0.2732365,0.07054421,0.01308354,0.5588716],"study_design_scores_gemma":[0.00005708856,0.0001723069,0.001502579,0.00002904325,0.00004742359,0.0004579853,0.00008341773,0.7450171,0.2065813,0.03955878,0.006418398,0.00007459824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03967649,0.0001326632,0.945406,0.0001513999,0.00004448926,0.000104731,0.0001795295,0.01342719,0.0008774406],"genre_scores_gemma":[0.5508702,0.0001288014,0.4442267,0.000172179,0.00005818615,0.000161967,0.0006512051,0.001740783,0.001989971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002208235,"threshold_uncertainty_score":0.009521723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04194995686318224,"score_gpt":0.2952075069351063,"score_spread":0.2532575500719241,"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."}}