Deep Learning Detection Method of Encrypted Malicious Traffic for Power Grid
Lin Chen, Yixi Jiang, Xiaoyun Kuang, Aidong Xu · 2020
The construction of digital power grid is the key task of China Southern Power Grid Corporation. However, with the application of new technologies such as “cloud, big data and intelligence”, the risks of network security are becoming diversified and complicated. In order to improve the defense and detection ability of advanced attacks, especially the crypted attacks, this paper proposes a detection technology of encrypted malicious traffic, through the use of deep neural network for feature learning and recognition, so as to improve the situation awareness ability in security of digital grid network.