基于深度神经网络的电力无线终端安全接入测试

Access security testing for wireless power terminals based on DNN

新型电力系统的外部无线终端容易被攻击者通过物理接触发动内部网络渗透攻击,传统的设备安全测试对提升接入设备的安全性能收效甚微,且容易产生较高的假阳率。提出一种基于深度神经网络的无线接入安全测试方法,采用堆叠稀疏自编码器实现测试数据集的特征降维,并选取合适的特征维数进行训练,将选取的特征作为深度神经网络的输入层,构建高效测试用例,监测并发现异常状态。实验表明,该方法准确率达到90%,可以高效发现电力移动设备接入环境的异常。

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.