计算机科学
跨站点脚本
SQL注入
支持向量机
Web应用程序安全性
机器学习
人工智能
朴素贝叶斯分类器
缓冲区溢出
Web应用程序
编码器
计算机安全
Web服务
万维网
计算机网络
Web开发
搜索引擎
操作系统
按示例查询
Web搜索查询
作者
Razaq Jinad,Kanupriya Gupta,Chukwuemeka Ihekweazu,Qingzhong Liu,Bing Zhou
标识
DOI:10.1007/978-3-031-36822-6_28
摘要
As web attacks continue to evolve, web applications are increasingly vulnerable to various security threats and network attacks. Malicious actors can inject harmful code in an HTTP request to launch attacks like SQL injection, XSS, buffer overflow, and others. Detecting and classifying unknown web attacks is essential for enhancing the reliability and security of web applications. In this study, we employ a Transformer called Bidirectional Encoder Representations (BERT) and several machine learning techniques (CNN, SVM, Random Forest, Naive Bayes, etc.) to categorize HTTP requests based on their attack type. We then compare the results obtained from all the techniques and observe that BERT achieves the highest accuracy of 99% compared to all other classification methods used.
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