426 research outputs found
A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions
One of the significant challenges that smart grid networks face is
cyber-security. Several studies have been conducted to highlight those security
challenges. However, the majority of these surveys classify attacks based on
the security requirements, confidentiality, integrity, and availability,
without taking into consideration the accountability requirement. In addition,
some of these surveys focused on the Transmission Control Protocol/Internet
Protocol (TCP/IP) model, which does not differentiate between the application,
session, and presentation and the data link and physical layers of the Open
System Interconnection (OSI) model. In this survey paper, we provide a
classification of attacks based on the OSI model and discuss in more detail the
cyber-attacks that can target the different layers of smart grid networks
communication. We also propose new classifications for the detection and
countermeasure techniques and describe existing techniques under each category.
Finally, we discuss challenges and future research directions
Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts
Smart Grid has rapidly transformed the centrally controlled power system into
a massively interconnected cyber-physical system that benefits from the
revolutions happening in the communications (e.g. 5G) and the growing
proliferation of the Internet of Things devices (such as smart metres and
intelligent electronic devices). While the convergence of a significant number
of cyber-physical elements has enabled the Smart Grid to be far more efficient
and competitive in addressing the growing global energy challenges, it has also
introduced a large number of vulnerabilities culminating in violations of data
availability, integrity, and confidentiality. Recently, false data injection
(FDI) has become one of the most critical cyberattacks, and appears to be a
focal point of interest for both research and industry. To this end, this paper
presents a comprehensive review in the recent advances of the FDI attacks, with
particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts
in the Smart Grid infrastructure. This review paper aims to provide a thorough
understanding of the incumbent threats affecting the entire spectrum of the
Smart Grid. Related literature are analysed and compared in terms of their
theoretical and practical implications to the Smart Grid cybersecurity. In
conclusion, a range of technical limitations of existing false data attack
research is identified, and a number of future research directions is
recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl
Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid
The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources.
This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements.
Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters
Detection of Non-Technical Losses in Smart Distribution Networks: a Review
With the advent of smart grids, distribution utilities have
initiated a large deployment of smart meters on the premises of the
consumers. The enormous amount of data obtained from the consumers
and communicated to the utility give new perspectives and possibilities
for various analytics-based applications. In this paper the current
smart metering-based energy-theft detection schemes are reviewed and
discussed according to two main distinctive categories: A) system statebased,
and B) arti cial intelligence-based.Comisión Europea FP7-PEOPLE-2013-IT
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