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Smart grids have become susceptible to cyber-attacks, being one of the most diversified cyberphysical systems. Measurements collected by the supervisory control and data acquisition system can be compromised by a smart hacker, who can cheat a bad-data detector during state estimation by injecting biased values into the sensor-collected measurements. This may result in false control decisions, compromising the security of the smart grid, and leading to financial losses, power network disruptions, or a combination of both. To overcome these problems, we propose a novel approach to cyber-attacks detection, based on an extremely randomized trees algorithm and kernel principal component analysis for dimensionality reduction. A performance evaluation of the proposed scheme is done by using the standard IEEE 57-bus and 118-bus systems. Numerical results show that the proposed scheme outperforms state-of-art approaches while improving the accuracy in detection of stealth cyber-attacks in smart-grid measurements.

Security is considered a critical issue in the deployment of 5G networks because of the vulnerability of information that can be intercepted by eavesdroppers in wireless transmission environments. Thus, physical layer security has emerged as an alternative for the secure enabling of 5G technologies and for tackling this security issue. In this paper, we study the secrecy energy efficiency (SEE) in a downlink underlying cognitive radio (CR)—non-orthogonal multiple access (NOMA) system with a cooperative relay. The system has an energy-harvesting (EH) user and an eavesdropper, where the transmitter provides direct communication with a close secondary user and a distant secondary user via the relay. Our objective is to maximize the SEE of the CR-NOMA system under the constraints of a minimum information rate for the secondary users, a minimum amount of energy harvested by the EH user, and maximum power availability at the transmitter and the relay that still prevents them from causing unacceptable interference with the primary user. The proposed solution to maximize the SEE is based on the low-computational—complexity particle swarm optimization (PSO) algorithm. For validation purposes, we compare the optimization outcomes obtained by the PSO algorithm with the optimal exhaustive search method. Furthermore, we compare the performance of our proposed CR-NOMA scheme with the conventional orthogonal multiple access (OMA) scheme.

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