使用千平方特征选择和机器学习分类器进行增强的SQL注入检测
Emanuel Casmiry1, Neema Mduma1, Ramadhani Sinde1
1Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania.
Frontiers in big data
|December 5, 2025
概括
这项研究增强了使用Chi-square特征选择和机器学习的结构化查询语言 (SQL) 注入检测. 功能选择显著提高了分类器的性能,决策树实现了99.73%的准确性,用于强大的网络攻击预防.
科学领域:
- 网络安全和机器学习
- 数据科学和网络安全数据科学和网络安全
背景情况:
- 结构化查询语言 (SQL) 注入攻击是一种普遍且昂贵的网络威胁,占全球网络攻击费用的20%以上.
- 由于这些攻击的动态性质,现有的SQL注入检测方法与高假阳性率和低于最佳准确性作斗争.
研究的目的:
- 通过将Chi-square特征选择与机器学习模型集成,提出一种用于检测SQL注入漏洞的增强方法.
- 评估特征选择对各种分类算法的性能影响,以提高SQL注入检测准确度.
主要方法:
- 通过将定制数据与SQLiV3.csv数据集合并,创建了一个混合数据集.
- 术语频率-反向文档频率 (TF-IDF) 用于将SQL查询转换为数值特征向量.
- 应用了千平方特征选择来识别和保留最相关的特征,随后测试了五个机器学习分类器 (多项纳夫贝叶斯,SVM,后勤回归,决策树,KNN).
主要成果:
- 千平方特征选择通过减轻噪音和删除多余特征,在所有测试模型中明显提高了分类性能.
- 决策树和K-最近邻居 (KNN) 模型在特征选择后表现出显著的性能增长.
- 决策树分类器实现了最高的准确性 (99.73%),精度 (99.72%),回忆 (99.70%),和F1得分 (99.71%),具有较低的假阳性率 (0.25%) 和错误分类率 (0.27%).
结论:
- 特性选择对于提高准确性和降低高维数据环境中的假阳性率至关重要,用于SQL注入检测.
- 该研究确立了特征选择是开发可靠和有效的SQL注入检测系统的重要组成部分,特别是用于改进集合和基于树的模型.
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