This article introduces a novel physical-layer (PHY-layer) authentication scheme, called transfer learning-based PHY-layer authentication (TL-PHA), aiming to achieve fast online user authentication that is highly desired for latency-sensitive applications such as edge computing. The proposed TL-PHA scheme is characterized by incorporating with a novel convolutional neural network architecture, namely, the triple-pool network (TP-Net), for achieving lightweight and online classification, as well as effective data augmentation methods for generation of data set samples for the network model training. To assess the performance of the proposed scheme, we conducted two sets of experiments, including the one using computer-simulated channel data and the other utilizing real experiment data generated by our wireless testbed. The results demonstrate the superiority of the proposed scheme in terms of authentication accuracy, detection rate, and training complexity compared to all the considered counterparts.

In this paper, we propose a clustering based physical-layer authentication scheme (CPAS) to overcome the drawback of traditional cipher-based authentication schemes that suffer from heavy costs and are limited by energy-constrained intelligent devices. CPAS is a novel cross-layer secure authentication approach for edge computing system with asymmetric resources. The CPAS scheme combines clustering and lightweight symmetric cipher with physical-layer channel state information to provide two-way authentication between terminals and edge devices. By taking advantage of temporal and spatial uniqueness in physical layer channel responses, the non-cryptographic physical layer authentication techniques can achieve fast authentication. The lightweight symmetric cipher initiates user authentication at the start of a session to establish the trust connection. Based on theoretical analysis, the CPAS scheme is secure and simple, but there is no trusted party, while it can also resist small integer attacks, replay attacks, and spoofing attacks. Besides, experimental results show that the proposed scheme can boost the total success rate of access authentication and decrease the data frame loss rate, without notable increase in authentication latencies.

With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is used to make a short-term prediction of FDIA, and the infinite norm and 2-norm are utilized to generate the classification detector. To assess the performance of the proposed method, we conducted experiments by the IEEE 14-bus system power grid model. The experimental results demonstrate that the method based on VAR model has a better detection of FDIA compared to the method based on auto-regressive (AR) model.

In this letter, we investigate a robust transmit optimization design based on an artificial noise (AN)-aided precoding scheme for maximizing secrecy rate in a multiple-input multiple-output multiple-eavesdropper (MIMOME) multicast network where a transmitter sends confidential information to multiple intended users and provides energy for multiple idle nodes while multiple passive eavesdroppers try to overhear the secret information. Supposing Gaussian CSI uncertainties, the proposed outage-constrained secrecy rate maximization (OC-SRM) problem is nonconvex and its analytical analysis is more challenging than several existing OC-SRM schemes. Technically, to circumvent this mathematically intractable secrecy problem, we resort to low SNR approximation and some conservative approximations (e.g., Bernstein-type inequality (BTI) and Sphere Bounding) to reformulate the OC-SRM problem into two convex subproblems which can be efficiently solved by the off-the-shelf optimization solver. Simulation results are provided to validate the security performance of the robust AN-aided precoding design.

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments.

Due to the requirement of real time controlling on the vehicular ad-hoc networks (VANETs), the security measure should be fast, lightweight, and strong. In this paper, we investigate the secure communication between the on-board unit and the roadside unit which has strong computing ability to remedy the insufficient computing power of on-board unit. The novel schemes are developed to guarantee both security and energy harvest. Specifically, a muti-antenna roadside unit (PT) conveys a secret message to a muti-antenna primary onboard unit (PR) intercepted by multiple muti-antenna adversaries (Eves). Simultaneously, a secondary roadside unit (ST) adopts simultaneous wireless information and power transfer (SWIPT) strategy to send secret-free data embeded with energy-carrying artificial noise (AN) to its service subscriber (SR), by which secrecy guarantee is provided for the primary on-board unit and the quality of service (QoS) is satisfied, i.e., data traffic and harvested energy. Technically, considering the secondary link's data transmission rate, harvested energy, and transmit power, we propose joint power allocation designs for the nonconvex secrecy rate maximization (SRM) problems under either perfect channel state information (CSI) or norm-bounded channel uncertainty model. Simulation results show that the proposed secondary linkaided secrecy designs can achieve better performance in terms of secrecy rate and energy efficiency.