An Intrusion Detection Method for Enterprise Network Based on Backpropagation Neural Network

Network security, as the prerequisite for the normal operation of enterprise network, should not focus on a single point, but all aspects of the network, ranging from physics, network, system, application to management. To ensure enterprise network security and prevent network attacks, it is of great importance to build an intrusion detection system (IDS) capable of protecting the network and computers from malicious attacks based on the Internet or host. In light of the above, this paper puts forward an intrusion detection method for enterprise network based on backpropagation neural network (BPNN), and carries out Python simulation of the proposed method on four problems, namely, normal state, the SYN flood (denial-of-service attack), snoop (unauthorized access from a remote host), and saint (reconnaissance attack). The simulation results show that the BPNN-based method could effectively check the network security environment, and accurately identify and detect intrusions. © 2020 International Information and Engineering Technology Association. All rights reserved.