{"id":"W4389575933","doi":"10.1109/csnet59123.2023.10339758","title":"Feature Engineering for Injection Attack Detection: An Exploration from SQLI to XSS","year":2023,"lang":"en","type":"article","venue":"","topic":"Web Application Security Vulnerabilities","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Preprocessor; Anomaly detection; Feature engineering; SQL injection; Feature (linguistics); Generalizability theory; Artificial intelligence; Support vector machine; Pattern recognition (psychology); Feature extraction; Detector; Data mining; Query by Example; Information retrieval; Mathematics; Deep learning","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":[0.001079327,0.0007335882,0.0005575115,0.00261723,0.0003988443,0.00102543,0.0007606166,0.0004683046,0.0007555392],"category_scores_gemma":[0.006834272,0.000185301,0.0007965978,0.002048778,0.0004771397,0.001557727,0.0008725829,0.001186011,0.0003731288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004340186,"about_ca_system_score_gemma":0.0007294738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002679068,"about_ca_topic_score_gemma":0.003627494,"domain_scores_codex":[0.9988632,0.0002250449,0.0001204572,0.0002585527,0.0004468468,0.0000858301],"domain_scores_gemma":[0.9947017,0.002746611,0.000542313,0.001031732,0.000862256,0.0001153708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007680737,0.001082108,0.09753291,0.0007103907,0.0002074206,0.001449107,0.0007085976,0.02605786,0.04718575,0.003418562,0.01117151,0.8097076],"study_design_scores_gemma":[0.0001001834,0.001607565,0.1098119,0.0002774425,0.0002377236,0.004612264,0.001164294,0.6545954,0.1731116,0.01734798,0.03695409,0.0001796473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7678297,0.003324397,0.2035578,0.001742078,0.0001562866,0.0003517395,0.004172376,0.01472738,0.004138339],"genre_scores_gemma":[0.8733025,0.0007597225,0.1202006,0.0002280194,0.00004123697,0.0001163662,0.004156663,0.0003357817,0.0008591065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002679068,"threshold_uncertainty_score":0.005708039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05828295819444965,"score_gpt":0.3010427301337992,"score_spread":0.2427597719393496,"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."}}