With advancements in renewable energy technologies, microgrids have evolved with distinctive cyber-physical system (CPS) characteristics, providing a dynamic and efficient control framework. However, the susceptibility of agents to false data injection attacks (FDIAs) during information transmission poses a notable security challenge. Existing efforts focus on detecting these attacks through machine learning methods, without regard to the cyber information embedded in the CPS communications. To address this gap, we propose a cyber-physical collaborative detection method (CPCGD) based on gate recurrent unit (GRU) and deep-learning neural network (DNN) to detect FDIAs, where the GRU is employed to capture temporal features in the physical domain, and the DNN is dedicated to capturing statistical features in the cyber domain. Moreover, a hierarchical detection and dynamic thresholding mechanism is presented to compensate for the poor performance of traditional distributed agents in microgrid environments. The experimental results and analysis demonstrate that the FDIA detection accuracy of the proposed scheme is better than several other benchmark detectors, and verify the effectiveness of the proposed scheme in cross-domain information detection.

The growth of distributed generation (DG) has established a connection between photovoltaic power generation and economic benefits. However, some users exploit this by engaging in photovoltaic power theft through network attacks on smart meters (SM) to manipulate power readings for financial gain. To tackle this problem, this paper proposes a data-driven stacking model for detecting photovoltaic power theft. The proposed method involves preprocessing real power generation data and designing attack functions to generate malicious data. Photovoltaic power generation features are categorized using the Person correlation coefficient. The stacking model combines three machine learning(ML) algorithms (Artificial Neural Networks, Random Forest, and XGBoost) as base predictors, with Support Vector Regression acting as the meta-predictor to accurately estimate power readings. The anomaly classification threshold is optimized using Sequential Model-Based Optimization based on residuals. Bayesian probability analysis updates the detection probability and makes decisions regarding power theft. Evaluation results demonstrate that the ensemble models outperform individual nonlinear models, highlighting the effectiveness of the proposed approach. Overall, this research presents a comprehensive solution for detecting photovoltaic power theft using a data-driven stacking model, which surpasses individual nonlinear models in identifying anomalies in photovoltaic power generation data.

As communication networks and the Internet of Things (IoT) converge in water critical infrastructure, it facilitates smart online monitoring of water supply systems, but it also significantly increases the incidence of cyber attacks. Additionally, it becomes especially challenging to detect attacks timely as they become more sophisticated and multifaceted. To address this issue, we propose a stacking ensemble network detection model based on industrial processes in water treatment systems, where the attacks are identified through recognizing anomalies caused by the underlying dynamics of the industrial control system. In the proposed detection model, we utilize a multilayer perceptron (MLP) to facilitate multi-module training, taking into consideration the multi-stage process of the water conservancy system. Furthermore, we adopt Long Short-Term Memory Recurrent Neural Network (LSTM) to effectively integrate the predicted results. To optimize the performance of this model, we leverage a Population Based Training (PBT) of Neural Networks to finetune the parameters. For each sensor parameter, the residuals between the predicted and measured data are classified using a sliding window-based dynamic threshold to identify anomalies. Based on the Real Safe Water Treatment (SWaT) system dataset, we evaluate the accuracy of our proposed scheme with three other schemes. The experimental comparison results show that the accuracy of our scheme is better than those of all the other three schemes.

In traditional ground cellular systems, ground terminals (GTs) in marginal areas are often faced with performance bottlenecks due to they are too far from the macro base station (MBS). For some temporary and unexpected communication service requirements, it is uneconomical to deploy femtocell access points (FAPs) on the edge of cell. In this paper, we investigate a cellular system that utilizes a unmanned aerial vehicles (UAVs) with base station module unit as an airborne mobile base station to provide communication channel for the cell-edge users offloaded by the MBS. Compared with FAPs, UAV is cheaper and flexible which can provide better communication quality for the cell-edge GTs with better channel condition. Based on the theoretical model, we proposed plausible optimal algorithm to maximize the energy-efficiency (EE) of the UAV by jointly optimizing the spectrum allocation, flying speed and user partition between the UAV and MBS. Numerical results indicate that our design could achieve relatively higher energy-efficiency by exploiting optimal flight strategy and spectrum allocation strategy.