In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is proposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms, the deep neural network (DNN)-based sensor nodes’ authentication method, the convolutional neural network (CNN)-based sensor nodes’ authentication method, and the convolution preprocessing neural network (CPNN)-based sensor nodes’ authentication method, have been adopted to implement the PHY-layer authentication in IWSNs. Among them, the improved CPNN-based algorithm requires few computing resources and has extremely low latency, which enable a lightweight multi-node PHY-layer authentication. The adaptive moment estimation (Adam) accelerated gradient algorithm and minibatch skill are used to accelerate the training of the neural networks. Simulations are performed to evaluate the performance of each algorithm and a brief analysis of the application scenarios for each algorithm is discussed. Moreover, the experiments have been performed with universal software radio peripherals (USRPs) to evaluate the authentication performance of the proposed algorithms. Due to the trainings being performed on the edge sides, the proposed method can implement a lightweight authentication for the sensor nodes under the edge computing (EC) system in IWSNs.

In this paper, we investigate the security threats in mobile edge computing (MEC) of Internet of things, and propose a deep-learning (DL)-based physical (PHY) layer authentication scheme which exploits channel state information (CSI) to enhance the security of MEC system via detecting spoofing attacks in wireless networks. Moreover, three gradient descent algorithms are adopted to accelerate the training of deep neural networks, which enables smaller computation overheads and lower energy consumptions. In addition, the maximum likelihood function of multi-user authentication method is derived, which explains why cross entropy is chosen as the loss function. The vectorization cost function is also derived. The mini batch scheme and 2 regularization are adopted to improve training accuracy and avoid over-fitting, respectively. Moreover, the simulation and experimental results show that the DL-based PHY-layer authentication approaches can distinguish multiple legitimate edge nodes from malicious nodes and attacker by CSIs, effectively. Our proposed method supports a better performance compared with the traditional hypothesis test based method.INDEX TERMS Mobile edge computing (MEC), the Internet of things (IoT), PHY-layer authentication, deep neural network (DNN), multi-user.

It is an important task of system security management to configure terminal security policy reasonably in edge computing (EC) to implement necessary security protection for terminals and EC system. Therefore, this paper analyses the security goals and requirements of terminal access for multi-service EC systems under the power grid, conducts terminal security risk assessment based on the AHP algorithm, and proposes an edge computing platform terminal security configuration optimization strategy, creatively using the OS-ELM algorithm. A framework for cloud training and edge-side online learning to meet the real-time and lightweight communication needs of edge platforms.

Uninterrupted Maintenance of power grid is the development direction of power grid . Electricity load forecasting is the basis for non-stop maintenance and the key to power grid load migration. Therefore, this paper analyses the plans and requirements of non-stop maintenance and predicts the grid load based on the Elman algorithm, by which the load forecasts are given. A load migration model is proposed, which can select the optimal low-load area in all areas to meet the business needs of non-stop maintenance.

Edge computing can meet the needs of many industries in real-time business control, security and privacy protection. In the process of massive heterogeneous terminals accessing the network through edge devices, it is very challenging to achieve fast and reliable authentication. The physical layer authentication technology authenticates through the channel characteristics, which has the characteristics of lightweight, and can well adapt to the authentication scenario of massive heterogeneous terminal access. In order to identify malicious nodes, this paper proposes an attack identification scheme of malicious nodes under edge computing. The new channel response information vector is constructed by using the correlation between the channel information of consecutive frames. Two or more time slot channel frequency response vectors are averaged to obtain a new channel response vector. It has the advantages of low computational complexity and high recognition accuracy. And combined with the channel frequency response based on the deep neural network to identify malicious nodes, the data set in the factory environment is simulated.