| Title | Cyber-Attack Classification in Smart Grid via Deep Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Zhou, Liang, Ouyang, Xuan, Ying, Huan, Han, Lifang, Cheng, Yushi, Zhang, Tianchen |
| Conference Name | Proceedings of the 2Nd International Conference on Computer Science and Application Engineering |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6512-3 |
| Keywords | Deep Neural Network, Metrics, pubcrawl, Resiliency, Scalability, security, Smart grid, smart grid security |
| Abstract | Smart grid1 is a modern power transmission network. With its development, the computing, communication and physical processes is getting more and more connected. However, an adversary can destroy power production by attacking the power secondary equipment. Accurate and fast response to cyber-attacks is a prerequisite for stable grid operation. Therefore, it is critical to identify and classify attacks in the smart grid. In this paper, we propose a novel approach that utilizes machine learning algorithms to help classify cyber-attacks. We built a deep neural network (DNN) model and select the global optimal parameters to achieve high generalization performance. The evaluation result demonstrates that the proposed method can effectively identify cyber-attacks in smart grid with an accuracy as high as 96%. |
| URL | http://doi.acm.org/10.1145/3207677.3278054 |
| DOI | 10.1145/3207677.3278054 |
| Citation Key | zhou_cyber-attack_2018 |
Cyber-Attack Classification in Smart Grid via Deep Neural Network