A Nonnegativity-Constraint Sparse Stacked Denoising Autoencoder for Anomaly Detection in Electric Power Communication Network - Tao, Zhuo; Yan, Yong; Yang, Yang; Wang, Yuanyuan; Luo, Jiang (IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, 2020)
As the scale of the electric power communication network is getting larger and larger, network security issues are becoming more and more complex. as an effective protection method, network traffic anomaly detection can provide important technical support for network situation awareness. In some cases, plenty of labeled data are unavailable, which may lead to low detection accuracy. To deal with this problem, a nonnegativity-constraint sparse stacked denoising autoencoder(NSSDAE) is proposed. Meanwhile, we propose a method called dynamic parameter freezing(DPF) for parameter transfer, which allows to find the best performance that may exist between the freezing and fine-tuning within a layer by adjusting the variable alpha. And discriminative joint probability maximum mean discrepancy is introduced for distribution adaptation. The results for NSL-KDD show that the proposed NSSDAE-TL algorithm is effective.