Network Intrusion Detection Method Based on GAN Model
| Title | Network Intrusion Detection Method Based on GAN Model |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Liao, D., Huang, S., Tan, Y., Bai, G. |
| Conference Name | 2020 International Conference on Computer Communication and Network Security (CCNS) |
| Keywords | adversarial idea, binary classification model, composability, Computational modeling, detection accuracy, Gallium nitride, gan, GAN model, generated adversarial network, Generative Adversarial Learning, generative adversarial networks, label sample set, label samples, learning (artificial intelligence), loss function, Metrics, multi-classification model, network intrusion detection, network intrusion detection method, neural nets, original classification model, parameter setting, pattern classification, performance indicators, Predictive Metrics, pubcrawl, resilience, Resiliency, Scalability, security of data, supervised learning, supervised learning multiclassification model, Training, training method, Transforms |
| Abstract | The existing network intrusion detection methods have less label samples in the training process, and the detection accuracy is not high. In order to solve this problem, this paper designs a network intrusion detection method based on the GAN model by using the adversarial idea contained in the GAN. The model enhances the original training set by continuously generating samples, which expanding the label sample set. In order to realize the multi-classification of samples, this paper transforms the previous binary classification model of the generated adversarial network into a supervised learning multi-classification model. The loss function of training is redefined, so that the corresponding training method and parameter setting are obtained. Under the same experimental conditions, several performance indicators are used to compare the detection ability of the proposed method, the original classification model and other models. The experimental results show that the method proposed in this paper is more stable, robust, accurate detection rate, has good generalization ability, and can effectively realize network intrusion detection. |
| DOI | 10.1109/CCNS50731.2020.00041 |
| Citation Key | liao_network_2020 |
- performance indicators
- Metrics
- multi-classification model
- network intrusion detection
- network intrusion detection method
- neural nets
- original classification model
- parameter setting
- pattern classification
- loss function
- pubcrawl
- resilience
- security of data
- supervised learning
- supervised learning multiclassification model
- Training
- training method
- Transforms
- Gallium nitride
- Resiliency
- Predictive Metrics
- Scalability
- adversarial idea
- binary classification model
- composability
- Computational modeling
- detection accuracy
- Generative Adversarial Learning
- gan
- GAN model
- generated adversarial network
- generative adversarial networks
- label sample set
- label samples
- learning (artificial intelligence)