{"id":"W3005146066","doi":"10.1109/tetc.2020.2971251","title":"PESKEA: Anomaly Detection Framework for Profiling Kernel Event Attributes in Embedded Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computing","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Petroleum Technology Development Fund","keywords":"Computer science; Anomaly detection; Profiling (computer programming); TRACE (psycholinguistics); Software; Data mining; Kernel (algebra); Exploit; Software system; Embedded system; Real-time computing; Operating system","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":[0.002103778,0.00194558,0.001368635,0.004686095,0.0005908672,0.001570582,0.002439993,0.001109553,0.001112645],"category_scores_gemma":[0.009846099,0.0005454947,0.00128309,0.002460869,0.0004421253,0.002124385,0.001770925,0.001919534,0.001011829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007420834,"about_ca_system_score_gemma":0.00117759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004470481,"about_ca_topic_score_gemma":0.00799202,"domain_scores_codex":[0.9979271,0.0002784852,0.0002133567,0.0007255272,0.0007027057,0.0001529481],"domain_scores_gemma":[0.9965527,0.001462294,0.0006259615,0.0005549758,0.0006384834,0.000165521],"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":[0.001406099,0.001383446,0.130518,0.001441239,0.001342242,0.0008850317,0.0006897161,0.18174,0.03898801,0.009375841,0.0561682,0.5760621],"study_design_scores_gemma":[0.00006657047,0.0002536144,0.01981031,0.00004823607,0.00008758847,0.0004155956,0.0001175511,0.9439844,0.01122423,0.01304497,0.01086796,0.0000789508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09033255,0.001886881,0.7882656,0.0004834337,0.0001989095,0.0005601152,0.01593682,0.1009994,0.001336245],"genre_scores_gemma":[0.5039198,0.0005145727,0.4599085,0.000277868,0.0001481625,0.0006717692,0.03114669,0.001429302,0.001983444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004686095,"threshold_uncertainty_score":0.01112598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03309358549518451,"score_gpt":0.2951683654935416,"score_spread":0.2620747799983572,"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."}}