| Title |
GAN-based Intrusion Detection Data Enhancement |
| ID_Doc |
44439 |
| Authors |
Fu, W; Qian, LP; Zhu, XH |
| Title |
GAN-based Intrusion Detection Data Enhancement |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/CCDC52312.2021.9602568 |
| Abstract |
In view of the lack of intrusion detection data and the slow update of mainstream detection methods, an intrusion detection data generation method based on a generative adversarial network is proposed. First, the overall data is digitized and normalized to maintain the integrity of the data; Then use the ACGAN model to learn the hidden features of the data and generate new data; Finally, evaluate the similarity and validity of the generated data from multiple perspectives. Experimental results show that the data generated by this method has similar characteristics to the original data, and can be used to enhance the original data set to meet the needs of intrusion detection systems. |
| Author Keywords |
Smart City; Cyber Security; Intrusion Detection Data; Generative Adversarial Network |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000824370102155 |
| WoS Category |
Automation & Control Systems |
| Research Area |
Automation & Control Systems |
| PDF |
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