| Title | Power Message Generation in Smart Grid via Generative Adversarial Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Ying, Huan, Ouyang, Xuan, Miao, Siwei, Cheng, Yushi |
| Conference Name | 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) |
| Keywords | Biological neural networks, Data models, energy conversion rate, Gallium nitride, gan, generative adversarial network, generative adversarial networks, Generators, machine learning, Metrics, neural nets, power engineering computing, power message generation, power supply quality, power system, power system security, power utilization rate, privacy, pubcrawl, Resiliency, Scalability, security, Smart grid, smart grid security, Smart grids, smart power grids, Training |
| Abstract | As the next generation of the power system, smart grid develops towards automated and intellectualized. Along with the benefits brought by smart grids, e.g., improved energy conversion rate, power utilization rate, and power supply quality, are the security challenges. One of the most important issues in smart grids is to ensure reliable communication between the secondary equipment. The state-of-art method to ensure smart grid security is to detect cyber attacks by deep learning. However, due to the small number of negative samples, the performance of the detection system is limited. In this paper, we propose a novel approach that utilizes the Generative Adversarial Network (GAN) to generate abundant negative samples, which helps to improve the performance of the state-of-art detection system. The evaluation results demonstrate that the proposed method can effectively improve the performance of the detection system by 4%. |
| DOI | 10.1109/ITNEC.2019.8729022 |
| Citation Key | ying_power_2019 |
Power Message Generation in Smart Grid via Generative Adversarial Network