Unlike the existing research that focuses on detecting electricity theft cyber-attacks in the consumption domain, this paper investigates electricity thefts at the distributed generation (DG) domain. In this attack, malicious customers hack into the smart meters monitoring their renewable-based DG units and manipulate their readings to claim higher supplied energy to the grid and hence falsely overcharge the utility company. Deep machine learning is investigated to detect such a malicious behavior. We aim to answer three main questions in this paper: a) What are the cyber-attack functions that can be applied by malicious customers to the generation data in order to falsely overcharge the utility company? b) What sources of data can be used in order to detect these cyber-attacks by the utility company? c) Which deep machine learning-model should be used in order to detect these cyber-attacks? Our investigation revealed that integrating various data from the DG smart meters, meteorological reports, and SCADA metering points in the training of a deep convolutional-recurrent neural network offers the highest detection rate (99.3%) and lowest false alarm (0.22%).

Designing an electricity theft cyberattack detector for the advanced metering infrastructures (AMIs) is challenging due to the limited availability of electricity theft datasets (i.e., malicious datasets). Anomaly detectors, which are trained solely on honest customers’ energy consumption profiles (i.e., benign datasets), could potentially overcome this challenge. Unfortunately, existing anomaly detectors in AMIs present shallow architectures and are incapable of capturing the temporal correlations as well as the sophisticated patterns present in the electricity consumption data, which impact their detection performance negatively. This article proposes the adoption of deep (stacked) autoencoders with a long-short-term-memory (LSTM)-based sequence-to-sequence (seq2seq) structure. The depth of the autoencoders’ structure helps capture the sophisticated patterns of the data and the seq2seq LSTM model enables exploitation of the time-series nature of data. We investigate the performance of simple autoencoder, variational autoencoder, and autoencoder with attention (AEA), in which improved detection performance is observed when seq2seq structures are adopted compared to fully connected ones. Based on the simulation results, the AEA detector offers an enhancement of $4\!-\!21\%$ and $4\!-\!13\%$ in terms of detection rate and false alarm rate, respectively, compared to existing state-of-the-art detectors

The development of tiny, low-cost, low-power and multifunctional sensor nodes equipped with sensing, data processing, and communicating components, have been made possible by the recent advances in micro-electro-mechanical systems (MEMS) technology. Wireless sensor networks (WSNs) assume a collection of such tiny sensing devices connected wirelessly and which are used to observe and monitor a variety of phenomena in the real physical world. Many applications based on these WSNs assume local clocks at each sensor node that need to be synchronized to a common notion of time. This paper reviews the existing clock synchronization protocols for WSNs and the methods of estimating clock offset and clock skew in the most representative clock synchronization protocols for WSNs.

False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid.

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors’ knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this article to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a graph neural network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern ac power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN-based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in $F$ 1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.

This paper proposes an efficient planning algorithm for allocating smart electric vehicle (EV) charging stations in remote communities. The planning problem jointly allocates and sizes a set of distributed generators (DGs) along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging. The planning algorithm specifies optimal locations and sizes of the EV charging stations and DG units that minimize two conflicting objectives: 1) deployment and operation costs and 2) associated green house gas emissions, while satisfying the microgrid technical constraints. This is achieved by iteratively solving a multi-objective mixed integer non-linear program. An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting genetic algorithm. Given the allocation and sizing decisions, an inner sub-problem ensures smart, reliable, and eco-friendly operation of the microgrid by solving a non-linear scheduling problem. The proposed algorithm results in a Pareto frontier that captures the tradeoff between the conflicting planning objectives. Simulation studies investigate the performance of the proposed planning algorithm in order to obtain a compromise planning solution.