{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001968995,0.00009343272,0.0000827408,0.0001453596,0.0001207416,0.000207625,0.0002894209,0.00006736906,0.000006991585],"category_scores_gemma":[0.00006102458,0.00009753062,0.00003290419,0.0007256891,0.000005023488,0.001332244,0.00007623294,0.0000722575,0.0001537755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005701376,"about_ca_system_score_gemma":0.00001620394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004843363,"about_ca_topic_score_gemma":0.0002009584,"domain_scores_codex":[0.9991924,0.00001956382,0.0001080528,0.000367515,0.0001539362,0.0001585428],"domain_scores_gemma":[0.999238,0.0001145983,0.00002519489,0.0004310516,0.0001084109,0.00008270083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000078788,0.0001717843,0.0002171294,0.000124123,0.00007606795,0.000002633058,0.03110473,0.3720938,0.1431644,0.1434329,0.08502109,0.2245125],"study_design_scores_gemma":[0.0001745474,0.0001595864,0.001542926,0.00000565965,0.00000263919,0.000001470976,0.0004522555,0.8926473,0.03694272,0.004549562,0.06330355,0.0002177452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1073071,0.000003312695,0.88657,0.00361319,0.0004354868,0.000363466,0.000006126312,0.001639874,0.00006144035],"genre_scores_gemma":[0.9015729,0.000001316463,0.0960478,0.0003223336,0.0004738807,0.0006998493,0.00004229523,0.00001805585,0.0008215141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7942659,"threshold_uncertainty_score":0.3977183,"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."}}