This paper investigates the problem of event-triggered H∞ load frequency control (LFC) for multiarea power systems under hybrid cyber attacks, including denial-of-service (DoS) attacks and deception attacks. An event-triggered transmission scheme is developed under the DoS attacks to lighten the load of network bandwidth while preserving a satisfactory system performance. Then, a new switched system model accounting for the simultaneous presence of DoS attacks and stochastic deception attacks is established with respect to the LFC for multiarea power system. On the basis of the new model, sufficient conditions ensuring multiarea power system exponentially mean-square stable with prescribed H∞ performance are obtained by using Lyapunov stability theory. Furthermore, criteria for simultaneously designing the weighting matrix in event-triggered scheme and the controller gain matrix are derived by utilizing the linear matrix inequality technique. Finally, a three-area power system is simulated to demonstrate the usefulness of the approaches proposed in this paper.

This article focuses on the security control for Takagi-Sugeno (T-S) fuzzy systems with adaptive event-triggered mechanism (AETM) and multiple cyber-attacks, which include deception attacks and denial-of-service (DoS) attacks. A multiple cyber-attacks model is first established for T-S fuzzy systems by considering deception attacks and DoS attacks at the same time. An AETM is introduced to relieve the network load, where the threshold of event-triggering condition can be adaptively adjusted while preserving the system performance. Then a novel mathematical model for T-S fuzzy systems with multiple cyber-attacks and AETM is proposed first. Based on the built model, sufficient conditions to guarantee the exponentially mean square stability of the system are achieved by utilizing the Lyapunov stability theory. Moreover, the controller gains are derived with the help of a linear matrix inequality technique. Finally, simulated examples are presented for illustrating the effectiveness of the proposed method.

Spammers, who manipulate online reviews to promote or suppress products, are flooding in online commerce. To combat this trend, there has been a great deal of research focused on detecting review spammers, most of which design diversified features and thus develop various classifiers. The widespread growth of crowdsourcing platforms has created large-scale deceptive review writers who behave more like normal users, that the way they can more easily evade detection by the classifiers that are purely based on fixed characteristics. In this paper, we propose a hybrid semisupervised learning model titled hybrid PU-learning-based spammer detection (hPSD) for spammer detection to leverage both the users' characteristics and the user-product relations. Specifically, the hPSD model can iteratively detect multitype spammers by injecting different positive samples, and allows the construction of classifiers in a semisupervised hybrid learning framework. Comprehensive experiments on movie dataset with shilling injection confirm the superior performance of hPSD over existing baseline methods. The hPSD is then utilized to detect the hidden spammers from real-life Amazon data. A set of spammers and their underlying employers (e.g., book publishers) are successfully discovered and validated. These demonstrate that hPSD meets the real-world application scenarios and can thus effectively detect the potentially deceptive review writers.

This article studies the unmanned aerial vehicle (UAV)-assisted wireless powered network, where a UAV is dispatched to wirelessly charge multiple ground nodes (GNs) by using radio frequency (RF) energy transfer and then the GNs use their harvested energy to upload the sensed information to the UAV. At each moment, the UAV is scheduled to charge the GNs or only one GN is scheduled to upload its data. An optimization problem is formulated to minimize the average Age of Information (AoI) of the GNs by jointly optimizing the trajectory of the UAV and the scheduling of information transmission and energy harvesting of GNs. As the problem is a combinational optimization problem with a set of binary variables, it is difficult to be solved. Thus, it is modeled as a Markov problem with large state spaces and a deep ${Q}$ network (DQN)-based scheme is proposed to find its near-optimal solution on the basis of the deep reinforcement learning (DRL) framework. Two nets are structured with artificial neural network (ANN), where one is for evaluating the reward of the action performed in current state, and the other is for predicting realistic action. The corresponding state spaces, the efficient action spaces, and reward function are designed. Simulation results demonstrate the convergence of the proposed DQN scheme, which also show that the proposed DQN scheme gets much smaller average AoI than the three other known schemes. Moreover, by involving the energy punishment in the reward, the UAV may save its energy but yield higher AoI. Additionally, the effects of the packet size, the transmit power, and the distribution area of GNs on the GNs’ average AoI are also discussed, which are expected to provide some useful insights.