| Title | Research on Application of Convolutional Neural Network in Intrusion Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Yizhi |
| Conference Name | 2020 7th International Forum on Electrical Engineering and Automation (IFEEA) |
| Date Published | sep |
| Keywords | Artificial neural networks, Communication networks, convolutional neural networks, cyber physical systems, Deep Learning, feature extraction, Internet, Intrusion detection, Metrics, Neural Network, Neural networks, policy-based governance, pubcrawl, Resiliency, security |
| Abstract | At present, our life is almost inseparable from the network, the network provides a lot of convenience for our life. However, a variety of network security incidents occur very frequently. In recent years, with the continuous development of neural network technology, more and more researchers have applied neural network to intrusion detection, which has developed into a new research direction in intrusion detection. As long as the neural network is provided with input data including network data packets, through the process of self-learning, the neural network can separate abnormal data features and effectively detect abnormal data. Therefore, the article innovatively proposes an intrusion detection method based on deep convolutional neural networks (CNN), which is used to test on public data sets. The results show that the model has a higher accuracy rate and a lower false negative rate than traditional intrusion detection methods. |
| DOI | 10.1109/IFEEA51475.2020.00153 |
| Citation Key | li_research_2020 |
Research on Application of Convolutional Neural Network in Intrusion Detection