| Title | Network Intrusion Detection Model Based on Convolutional Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zheng, Shiji |
| Conference Name | 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) |
| Date Published | mar |
| Keywords | Adaptation models, artificial neural network, Artificial neural networks, Collaboration, convolutional neural network, convolutional neural networks, cyber physical systems, Deep Learning, Metrics, network intrusion detection, Neural Network Security, Neural networks, parameters, policy-based governance, pubcrawl, resilience, Resiliency, security, Training |
| Abstract | Network intrusion detection is an important research direction of network security. The diversification of network intrusion mode and the increasing amount of network data make the traditional detection methods can not meet the requirements of the current network environment. The development of deep learning technology and its successful application in the field of artificial intelligence provide a new solution for network intrusion detection. In this paper, the convolutional neural network in deep learning is applied to network intrusion detection, and an intelligent detection model which can actively learn is established. The experiment on KDD99 data set shows that it can effectively improve the accuracy and adaptive ability of intrusion detection, and has certain effectiveness and advancement. |
| DOI | 10.1109/IAEAC50856.2021.9390930 |
| Citation Key | zheng_network_2021 |
Network Intrusion Detection Model Based on Convolutional Neural Network