基于DCNN-GRU模型的XSS攻击检测方法

为了提高跨站脚本攻击的检测效率,利用一维DCNN快速处理时序问题的能力和GRU模型处理上下文具有长期依赖关系问题的能力,提出基于DCNN-GRU模型的XSS攻击检测方法.对原始数据做规范化处理,将数据转化为可以对深度学习网络模型进行输入的特征向量.通过卷积层和池化层处理特征向量,GRU层作为门控机制来保留代码间的依赖关...

XSS ATTACK DETECTION METHOD BASED ON DCNN-GRU MODEL

The one-dimensional DCNN can process timing problem quickly,and the GRU model can process context problems which have long time dependence relationship.In order to improve the efficiency of detection of the XSS attack,these two deep learning models are combined based on their ability,and an XSS attack detection method is proposed based on DCNN-GRU model.The raw data was standardized,and it could transform into feature vectors aim to fit the deep learning network models.Feature vectors were processed by the convolutional layer and the pooling layer,and the GRU layer was used as a gating mechanism to preserve the dependencies between the codes.It used the fully connected layer to complete the normalization process,and the attack detection was achieved by using the Softmax classifier to realize classification.Comparative experiments were carried out by using foxscheduler dataset.The experimental results show that the DCNN-GRU model has shorter training time,and its accuracy,recall and F1 values are the highest,compared with the single DCNN,GRU,LSTM models and SVM model in machine learning which verified the detection effect of the DCNN-GRU model.