基于深度迁移学习的网络入侵检测.

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. [ABSTRACT FROM AUTHOR]