Smart grid as the future of power system improves efficiency, flexibility, and reliability by employing the information and communication technologies. However, these new technologies cause some challenges in security issues in the smart grid. In addition to the active and passive attacks in the communication link, such as tampering and eavesdropping, the physical attacks can also be performed on smart meters. In the absence of hardware protection, physical attacks could easily disrupt the major performance of the system. On the other hand, the security schemes must be designed as lightweight as possible according to the constrained resource of the smart meters. To solve the mentioned problems simultaneously, this article proposes a secure and lightweight protocol based on physically unclonable function for communication between the neighborhood gateways and smart meters. The security analysis shows that the proposed scheme not only resists against the all possible cyber and physical attacks, but also provides confidentiality, integrity, and mutual authentication. Moreover, the comparative performance evaluation demonstrates that the proposed scheme is efficient in terms of usage of storage space, communication overhead, and computational cost.

Vehicle-to-grid (V2G) technology has become a promising concept for the near future smart grid eco-system. V2G improves smart grid resiliency by enabling two-way communication and electricity flows while reducing the greenhouse gases emission. V2G practicality and stability is strongly based on the exchanged data between electrical vehicles (EVs) and the grid server (GS). However, using communication protocols to exchange vital information leads grid to being vulnerable against various types of attack. To prevent the well-known attacks in V2G network, this paper proposes a privacy-aware authentication scheme that ensures data integrity, confidentiality, users’ identity and location privacy, mutual authentication, and physical security based on physical unclonable function (PUF). Furthermore, the performance analysis shows that the proposed scheme outperforms the state-of-the-art, since EVs only use lightweight cryptographic primitives for every protocol execution.

In this paper, we explore the communication performance of vehicle-to-vehicle (V2V) networks assisted by a reconfigurable intelligent surface (RIS) and a simultaneous transmitting and reflecting intelligent omni-surface (STAR-IOS) under non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes. In particular, we consider that the RIS is close to the transmitter vehicle while the STAR-IOS is near the receiver vehicles. In addition, it is assumed that the STAR-IOS exploits the energy-splitting (ES) protocol for communication and the fading channels between the RIS and STAR-IOS follow composite Fisher-Snedecor $\mathcal {F}$ distribution. Under such assumptions, we first use the central limit theorem (CLT) to derive the PDF and the CDF of equivalent channels at receiver vehicles, and then, we derive the closed-form expression of outage probability (OP) under NOMA/OMA scenarios. Additionally, by exploiting Jensen's inequality, we propose an upper bound of the ergodic capacity (EC) and subsequently develop an analytical expression of the energy efficiency (EE) for both NOMA and OMA cases. Further, our analytical results, which are double-checked with the Monte-Carlo simulation, reveal that applying RIS/STAR-RIS in V2V communications significantly enhances the communication performance of intelligent transportation systems (ITS). Besides, the results indicate that considering the NOMA scheme provides better performance in terms of the OP, EC, and EE as compared with the OMA case for the considered V2V communication system.

A smart grid (SG) is an advanced electrical grid that enhances the efficiency and reliability of traditional power grids. Reconfigurable intelligent surfaces (RISs) have been recently proposed to enhance communication performance in the SG. However, the communication links between different SG components could suffer from eavesdropping and unauthorized access, which makes physical layer security (PLS) a promising solution for addressing these concerns. In this article, we focus on exploring the effect of applying RIS on enhancing the PLS performance of SG communications. Specifically, we consider an RIS with reflecting elements besides a smart meter, a neighborhood gateway (NG), and an eavesdropper to develop a smart environment in the SG to improve PLS performance in terms of secrecy outage probability (SOP) and average secrecy capacity (ASC). For this purpose, we first derive a probability density function and a cumulative distribution function for the signal-to-noise ratio (SNR) at both the NG and the eavesdropper. Then, we derive closed-form expressions of SOP and ASC to evaluate the impact of various system parameters on the secrecy performance of RIS-aided SG communications. Furthermore, considering the significance of system behavior under high-SNR conditions, we conduct an asymptotic analysis of the SOP and ASC. Finally, we apply the Monte Carlo simulation to validate the analytical results. Our results indicate that using the RIS can significantly enhance the secrecy performance of SG communications compared to the conventional SG scenarios without the RIS.

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.