{"id":"W2911153497","doi":"10.1109/rtcsa.2018.00035","title":"Hierarchical Attention-Based Anomaly Detection Model for Embedded Operating Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Anomaly detection; Executable; Debugging; Bespoke; Fault detection and isolation; Embedded operating system; A priori and a posteriori; Software system; Kernel (algebra); Software; Real-time operating system; Set (abstract data type); TRACE (psycholinguistics); Embedded system; Real-time computing; Data mining; Operating system; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002705938,0.00009456195,0.0000936219,0.00008747771,0.0003734395,0.0002251636,0.0003043496,0.00006462014,0.000004898983],"category_scores_gemma":[0.00001786013,0.00008481498,0.00007331183,0.000233526,0.00004045249,0.0002454578,0.00005041838,0.00006965157,0.00002035657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003547574,"about_ca_system_score_gemma":0.00006220163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003903107,"about_ca_topic_score_gemma":0.00002505786,"domain_scores_codex":[0.9991252,0.0000309252,0.0002210285,0.0003337772,0.0001161336,0.0001729286],"domain_scores_gemma":[0.9992462,0.00005310893,0.00006335058,0.0003664018,0.0002084279,0.0000625088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001954416,0.0002046093,0.0001421594,0.0000509297,0.00002637366,4.806439e-7,0.0002999064,0.01725346,0.1424726,0.8000704,0.001927226,0.03753231],"study_design_scores_gemma":[0.0001573864,0.0001413697,0.00008587237,0.000006776505,0.000003040695,0.000003747537,0.00001095042,0.9811122,0.01599338,0.00173007,0.0006410644,0.0001141334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009493157,0.000003990488,0.9870718,0.0005801842,0.0001055643,0.0004702564,0.000002723724,0.0006547953,0.001617602],"genre_scores_gemma":[0.7291411,2.940294e-7,0.2691525,0.0003553767,0.00007382459,0.0003542081,0.000001883563,0.000007530623,0.0009133003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9638588,"threshold_uncertainty_score":0.3458655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02409379219822335,"score_gpt":0.2805511547473958,"score_spread":0.2564573625491725,"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."}}