67 research outputs found
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
Reinforcement Learning Based Penetration Testing of a Microgrid Control Algorithm
Microgrids (MGs) are small-scale power systems which interconnect distributed
energy resources and loads within clearly defined regions. However, the digital
infrastructure used in an MG to relay sensory information and perform control
commands can potentially be compromised due to a cyberattack from a capable
adversary. An MG operator is interested in knowing the inherent vulnerabilities
in their system and should regularly perform Penetration Testing (PT)
activities to prepare for such an event. PT generally involves looking for
defensive coverage blindspots in software and hardware infrastructure, however
the logic in control algorithms which act upon sensory information should also
be considered in PT activities. This paper demonstrates a case study of PT for
an MG control algorithm by using Reinforcement Learning (RL) to uncover
malicious input which compromises the effectiveness of the controller. Through
trial-and-error episodic interactions with a simulated MG, we train an RL agent
to find malicious input which reduces the effectiveness of the MG controller
Improvise, Adapt, Overcome: Dynamic Resiliency Against Unknown Attack Vectors in Microgrid Cybersecurity Games
Cyber-physical microgrids are vulnerable to rootkit attacks that manipulate
system dynamics to create instabilities in the network. Rootkits tend to hide
their access level within microgrid system components to launch sudden attacks
that prey on the slow response time of defenders to manipulate system
trajectory. This problem can be formulated as a multi-stage, non-cooperative,
zero-sum game with the attacker and the defender modeled as opposing players.
To solve the game, this paper proposes a deep reinforcement learning-based
strategy that dynamically identifies rootkit access levels and isolates
incoming manipulations by incorporating changes in the defense plan. A major
advantage of the proposed strategy is its ability to establish resiliency
without altering the physical transmission/distribution network topology,
thereby diminishing potential instability issues. The paper also presents
several simulation results and case studies to demonstrate the operating
mechanism and robustness of the proposed strategy
Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey
The rapid development of information and communications technology has
enabled the use of digital-controlled and software-driven distributed energy
resources (DERs) to improve the flexibility and efficiency of power supply, and
support grid operations. However, this evolution also exposes
geographically-dispersed DERs to cyber threats, including hardware and software
vulnerabilities, communication issues, and personnel errors, etc. Therefore,
enhancing the cyber-resiliency of DER-based smart grid - the ability to survive
successful cyber intrusions - is becoming increasingly vital and has garnered
significant attention from both industry and academia. In this survey, we aim
to provide a systematical and comprehensive review regarding the
cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an
integrated threat modeling method is tailored for the hierarchical DER-based
smart grid with special emphasis on vulnerability identification and impact
analysis. Then, the defense-in-depth strategies encompassing prevention,
detection, mitigation, and recovery are comprehensively surveyed,
systematically classified, and rigorously compared. A CRE framework is
subsequently proposed to incorporate the five key resiliency enablers. Finally,
challenges and future directions are discussed in details. The overall aim of
this survey is to demonstrate the development trend of CRE methods and motivate
further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication
Consideratio
Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers
This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks
Bibliographical review on cyber attacks from a control oriented perspective
This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented.Peer ReviewedPostprint (author's final draft
Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers
This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks
Cyber-Physical Power System (CPPS): A Review on Modelling, Simulation, and Analysis with Cyber Security Applications
Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.publishedVersio
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