A Nonnegativity-Constraint Sparse Stacked Denoising Autoencoder for Anomaly Detection in Electric Power Communication Network

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 cas...

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Vydáno v:IEEE International Symposium on Broadband Multimedia Systems and Broadcasting s. 1 - 6
Hlavní autoři: Tao, Zhuo, Yan, Yong, Yang, Yang, Wang, Yuanyuan, Luo, Jiang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 27.10.2020
Témata:
Anomaly detection
Communication networks
few labeled data
Multimedia communication
Noise reduction
nonnegativity-constraint sparse stacked denoising autoencoder
parameter transfer learning
Power systems
Security
Telecommunication traffic
traffic anomaly detection
ISSN:2155-5052
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Shrnutí: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.
ISSN:2155-5052
DOI:10.1109/BMSB49480.2020.9379595