Intrusion Detection System Based on Adversarial Domain Adaptation Algorithm

With the explosive growth of the Internet, massive high-dimensional data and multiple attack types make intrusion detection systems face greater challenges. In practical application scenarios, the amount of abnormal data is small, and intrusion detection systems in different scenarios cannot be quickly migrated, and specific intrusion detection systems need to be trained for different scenarios, which greatly wastes manpower and material resources. Therefore, in view of the hierarchical characteristics of network data streams, this paper uses CNN and RNN networks to extract the spatiotemporal features of network data streams, then input them into GAN for unsupervised learning. Considering that long and short-term recurrent neural network (LSTM-RNN) has been shown to be able to obtain information and learn complex time series by remembering the backward (or even forward) time steps of cells, this paper replaces the generator and discriminator of GAN with LSTM-RNN. Anomaly detection is then performed based on residual loss and identification loss. Finally, this paper uses the deep domain adaptation algorithm to map the target domain and the source domain, and then optimizes the confusion loss of the domain by adversarial training, and finally extracts the invariant features of the target and the source domain.