发表机构
University of Reading（雷丁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究设计并优化了DistilBERT-堆叠集成模型，提升了SQL注入检测的准确率与鲁棒性，大幅降低推理延迟，为构建实时高效的Web应用防火墙提供了新方案。
AI 中文摘要
检测SQL注入（SQLi）攻击是Web应用安全领域最关键的挑战之一。本研究开展系统文献综述，明确该领域的研究缺口，针对性设计并优化了DistilBERT-堆叠集成管道，以提升检测效率与鲁棒性，同时降低误报率与漏报率。研究执行了全面的预处理与分词操作，提取DistilBERT嵌入，训练机器学习及集成分类器，并基于准确率、精确率、召回率与F1分数对其排序；将表现最佳的三个模型（逻辑回归、XGBoost、SVM）通过神经元学习器组合为堆叠集成模型。该集成模型经快速梯度符号法（FGSM）生成的对抗样本强化，并使用Optuna调优。优化后的集成模型在所有报告指标上达到99.81%，与最强单模型（DistilBERT-SVM，99.82%）表现接近。在本研究使用的评估平台（第3.8节）上，该集成模型对完整测试集的分类耗时为0.0136秒，而DistilBERT-SVM耗时1.896秒，推理延迟降低约140倍，同时在单步FGSM攻击下仍保持99.77%的准确率。本研究的贡献是设计并验证了一款SQLi检测器，其具备与当前最优水平相当的准确率、实时处理速度，且对单步FGSM攻击表现出鲁棒性，而非仅实现准确率的微小提升；敏感性分析进一步确认了模型的稳定性。这些发现凸显了对抗训练与堆叠元学习在构建用于SQLi检测的鲁棒Web应用防火墙（WAF）中的价值。为便于开放验证，数据集、测试集及模型已在指定URL提供。
英文摘要
Detecting SQL Injection (SQLi) attacks ranks among the most critical challenges in web application security. This research conducted a systematic literature review to identify the research gaps in this domain and responsively designed and optimised a DistilBERT-Stacked Ensemble pipeline to improve detection efficiency and robustness while reducing false-positive and false-negative rates. Comprehensive pre-processing and tokenisation were performed, DistilBERT embeddings were extracted, and machine-learning and ensemble classifiers were trained and ranked on accuracy, precision, recall and F1-score. The three best performers (Logistic Regression, XGBoost and SVM) were combined through a neural meta-learner to form a stacked ensemble. The ensemble was hardened with adversarial examples generated by the Fast Gradient Sign Method (FGSM) and tuned with Optuna. The optimised ensemble achieved 99.81% across all reported metrics, closely comparable to the strongest single model (DistilBERT SVM, 99.82%). On the evaluation platform used in this study (Section 3.8), the ensemble classified the full test set in 0.0136s against 1.896s for DistilBERT-SVM, an approximately 140-fold reduction in measured inference latency, while retaining 99.77% accuracy under a single-step FGSM attack. The contribution is the design and validation of a SQLi detector performing with state-of-the-art accuracy at real-time speed and with demonstrated robustness to a single-step FGSM attack, rather than a marginal gain in accuracy. Sensitivity analysis further confirmed the stability of the model. These findings highlight the value of adversarial training and stacked meta-learning in building robust Web Application Firewalls (WAFs) for SQLi detection. For open validation, the dataset, test sets and models are made available at https://github.com/mlily2024/Final-project-SQL-injection-pipeline.
Comments12 pages, 8 figures, 6 tables
