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- Title
基于深度神经网络的电力无线终端安全接入测试.
- Authors
吕 磅; 韩嘉佳; 孙 歆; 戴 桦; 李沁园; 孙昌华
- Abstract
The external wireless terminals of the new power system are susceptible to be attacked through internal network penetration triggered by physical contact. The traditional device security test has little effect on improving the security performance of the access devices and is prone to produce a high false positive rate (FPR). A wireless access security testing system based on deep neural networks (DNN) is proposed. The system adopts a stacked sparse autoencoder (SSAE) to realize the feature dimensionality reduction of the test dataset and selects the appro⁃ priate feature dimensions for training. The selected features are used as the input layer of the DNN to construct highly efficient test cases, as well as to monitor and discover the abnormal states. The experiment results show that the system has an accuracy rate of 90% and can efficiently detect anomalies in the access environment of wireless power terminals.
- Subjects
ARTIFICIAL neural networks; SECURITY systems; TEST systems; MACHINE learning
- Publication
Zhejiang Electric Power, 2023, Vol 42, Issue 10, p101
- ISSN
1007-1881
- Publication type
Article
- DOI
10.19585/j.zjdl.202310012