Biblio

Security is of vital importance in wireless industrial communication systems. When spoofing attacking has occurred, leading to economic losses or even safety accidents. So as to address the concern, existing approaches mainly rely on traditional cryptographic algorithms. However, these methods cannot meet the needs of short delay and lightweight. In this paper, we propose a CSI-based PHY-layer security authentication scheme to detect spoofing detection. The main idea takes advantage of the uncorrelated nature of wireless channels to the identification of spoofing nodes in the physical layer. We demonstrate a MIMO-OFDM based spoofing detection prototype in industrial environments. Firstly, utilizing Universal Software Radio Peripheral (USRPs) to establish MIMO-OFDM communication systems is presented. Secondly, our proposed security scheme of CSI-based PHY-layer authentication is demonstrated. Finally, the effectiveness of the proposed approach has been verified via attack experiments.

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of “edge intelligence”, which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies; Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application.

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.

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.

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.

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.