论文标题
使用深度学习的Web应用程序攻击检测
Web Application Attack Detection using Deep Learning
论文作者
论文摘要
现代Web应用程序由HTTP/HTTPS消息主导，其中由一个或多个标头组成，其中大多数利用和有效负载可以由攻击者注入。根据OWASP的说法，80％的Web攻击是通过HTTP/HTTPS请求查询进行的。在本文中，我们提出了基于深度学习的Web应用程序攻击检测模型。该模型使用自动编码器，可以根据它们从每个单词或字符的单词和权重序列中学习。分类引擎在ECML-KDD数据集上进行了培训，以针对特定攻击类型进行异常查询分类。拟议的Web应用检测引擎经过异常和良性Web查询训练，以达到1的接收器操作特征曲线的准确性。实验结果表明，所提出的模型可以检测Web应用程序以低的假阳性速率成功攻击。
Modern web applications are dominated by HTTP/HTTPS messages that consist of one or more headers, where most of the exploits and payloads can be injected by attackers. According to the OWASP, the 80 percent of the web attacks are done through HTTP/HTTPS requests queries. In this paper, we present a deep learning based web application attacks detection model. The model uses auto-encoder that can learn from the sequences of word and weight each word or character according to them. The classification engine is trained on ECML-KDD dataset for classification of anomaly queries with respect to specific attack type. The proposed web application detection engine is trained with anomaly and benign web queries to achieve the accuracy of receiver operating characteristic curve of 1. The experimental results show that the proposed model can detect web applications attack successfully with low false positive rate.
