Biblio

With the popularity of smart devices in the power grid and the advancement of data collection technology, the amount of electricity usage data has exploded in recent years, which is beneficial for optimizing service quality and grid operation. However, current data analysis is mainly based on cloud platforms, which poses challenges to transmission bandwidth, computing resources, and transmission delays. To solve the problem, this paper proposes a graph convolution neural networks (GCNs) based edge-cloud collaborative anomaly detection model. Specifically, the time series is converted into graph data based on visibility graph model, and graph convolutional network model is adopted to classify the labeled graph data for anomaly detection. Then a model segmentation method is proposed to adaptively divide the anomaly detection model between the edge equipment and the back-end server. Experimental results show that the proposed scheme provides an effective solution to edge anomaly detection and can make full use of the computing resources of terminal equipment.

By taking advantages of graphs and potential functions, a security reinforcement method for edge computing terminals is proposed in this paper. A risk graph of the terminal security protection system is constructed, and importance of the security protection and risks of the terminals is evaluated according to the topological potential of the graph nodes, and the weak points of the terminal are located, and the corresponding reinforcement method is proposed. The simulation experiment results show that the proposed method can upgrade and strengthen the key security mechanism of the terminal, improve the performance of the terminal security protection system, and is beneficial to the security management of the edge computing system.

In this paper, we mainly introduce the security protection technology of smart grid based on edge computing and propose an edge computing security protection architecture based on multi-service flexible mechanism. Aiming at the real time requirements of heterogeneous energy terminal access and power edge computing business in multiple interactive environment, a real-time and strong compatibility terminal security access mechanism integrating physical characteristics and lightweight cryptographic mechanism is proposed. According to different power terminal security data requirements, the edge computing data transmission, processing security and privacy protection technology are proposed. In addition, in the power system of distribution, microgrid and advanced metering system, the application of edge computing has been well reflected. Combined with encryption technology, access authentication, the security defense of edge data, edge equipment and edge application is carried out in many aspects, which strengthens the security and reliability of business penetration and information sharing at the edge of power grid, and realizes the end-to-end and end-to-system security prevention and control of power grid edge computing.

Internet of Things (IoT) enable information transmission and sharing among massive IoT devices. However, the key establishment and management in IoT become more challenging due to the low latency requirements and resource constrained IoT devices. In this work, we propose a practical physical layer based secret key sharing scheme for MIMO (multiple-input-multiple-output) IoT devices to reduce the communication delay caused by key establishment of MIMO IoT devices. This is because the proposed scheme attachs secret key sharing with communication simultaneously. It is achieved by the proposed MIMO self-injection AN (SAN) tranmsission, which is designed to deliberately maximum the receive SNR (signal to noise ratio) at different antenna of the legitimate IoT device, based on the value of secret key sharing to him. The simulation results verified the validity and security of the proposed scheme.

Binary code search has received much attention recently due to its impactful applications, e.g., plagiarism detection, malware detection and software vulnerability auditing. However, developing an effective binary code search tool is challenging due to the gigantic syntax and structural differences in binaries resulted from different compilers, compiler options and malware family. In this paper, we propose a scalable and accurate binary search engine which performs syntactic matching by combining a set of key techniques to address the challenges above. The key contribution is binary code searching technique which combined function filtering and partial trace method to match the function code relatively quick and accurate. In addition, a simhash and basic information based function filtering is proposed to dramatically reduce the irrelevant target functions. Besides, we introduce a partial trace method for matching the shortlisted function accurately. The experimental results show that our method can find similar functions, even with the presence of program structure distortion, in a scalable manner.

With the great development of the information communication technology, power systems have been typical Cyber Physical Systems (CPSs). Although the control function of the grid side is becoming more intelligent, Grid Cyber Physical System (GCPS) brings the risk of potential cyberattacks. In this paper, the impacts of cyber-attacks against GCPS are analyzed based on confusion matrix model firstly, then a double-layer cyber physical collaboration control strategy adjustment methods is proposed considering the status of cyber modules and physical devices infected by cyber-attacks. Finally, the feasibility and effectiveness of the proposed method are verified on the IEEE standard system.

This paper investigates the physical layer (PHY-layer) authentication that exploits channel state information (CSI) to enhance multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system security by detecting spoofing attacks in wireless networks. A multi-user authentication system is proposed using convolutional neural networks (CNNs) which also can distinguish spoofers effectively. In addition, the mini batch scheme is used to train the neural networks and accelerate the training speed. Meanwhile, L1 regularization is adopted to prevent over-fitting and improve the authentication accuracy. The convolutional-neural-network-based (CNN-based) approach can authenticate legitimate users and detect attackers by CSIs with higher performances comparing to traditional hypothesis test based methods.

A cross-layer secure communication scheme for multiple input multiple output (MIMO) system based on spatial modulation (SM) is proposed in this paper. The proposed scheme combined the upper layer stream cipher with the distorted signal design of the MIMO spatial modulation system in the physical layer to realize the security information transmission, which is called cross-layer secure communication system. Simulation results indicate that the novel scheme not only further ensure the legitimate user an ideal reception demodulation performance as the original system, but also make the eavesdropper' error rate stable at 0.5. The novel system do not suffer from a significant increasing complexity.

The smart grid aims to improve the efficiency, reliability and safety of the electric system via modern communication system, it's necessary to utilize cloud computing to process and store the data. In fact, it's a promising paradigm to integrate smart grid into cloud computing. However, access to cloud computing system also brings data security issues. This paper focuses on the protection of user privacy in smart meter system based on data combination privacy and trusted third party. The paper demonstrates the security issues for smart grid communication system and cloud computing respectively, and illustrates the security issues for the integration. And we introduce data chunk storage and chunk relationship confusion to protect user privacy. We also propose a chunk information list system for inserting and searching data.