基于深度迁移学习的网络入侵检测

为解决网络入侵检测问题，提高检测准确率和降低误报率，提出一种基于深度迁移学习的网络入侵检测方法，该方法使用非监督学习的深度自编码器来进行迁移学习，实现网络的入侵检测。首先对深度迁移学习问题进行建模，然后对深度模型进行迁移学习。迁移学习框架由嵌入层和标签层实现编/解码，编码和解码权重由源域和目标域共享，用于知识的迁移。嵌入层中，通过最小化域之间的嵌入实例的KL散度来强制源域和目标域数据的分布相似；在标签编码层中，使用softmax回归模型对源域的标签信息进行编码分类。实验结果表明，该方法能够实现网络入侵检测，且性能优于其他入侵检测方法。

Network intrusion detection based on deep transfer learning

In order to solve the problem of network intrusion detection, improve detection accuracy and reduce false positive rate, this paper proposed a network intrusion detection method based on deep transfer learning. This method used unsupervised learning deep self-encoder for transfer learning to realize network intrusion detection. Firstly, it modeled the deep transfer learning problem, and then modeled the deep transfer learning problem. The transfer learning framework implemented encoding and decoding by embedding layer and label layer, and shared the weight of encoding and decoding by source domain and target domain for knowledge transferring. In the embedding layer, it compelled the distribution of source domain and target domain data to be similar by minimizing the KL divergence of embedded instances between domains. In the label coding layer, it coded and classified the label information of source domain by using the softwaremax regression model. The experimental results show that this method can implement network intrusion detection, and its performance is better than other intrusion detection methods.