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CNN_for_XSS

说明:

本项目通过神经网络算法来检测web攻击。 由于Web攻击种类众多,本文主要选择OWASP TOP 10中的跨站脚本攻击(XSS)作为主要检测对象进行研究。 使用Word2Vec建立词向量模型,并使用TSNE算法进行降维,分别使用卷积神经网络、时间循环神经网络、多层神经网络三种算法检测入侵行为。

环境

tensorflow 1.13.1 tensorboard 1.13.1 keras 2.1.0

运行

word2vec_ginsim.py训练嵌入式词向量 processing.py预处理数据,生成训练数据和测试数据。 CNN.py使用卷积神经网络训练模型,在测试集上测试准确率和召回率。

2019.12.25更新

新增了长短期记忆网络(LSTM)来对比之前的卷积神经网络(CNN)在相同样本集下检测的准确率和召回率。未来还计划采用多层感知器(MLP)来进行检测。

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