State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in 'normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has …
A novel false data injection attack (FDIA) model against DC state estimation is proposed, which requires no network parameters and exploits only limited phasor measurement unit (PMU) data. The proposed FDIA model can target specific states and launch large deviation attacks using estimated line parameters. Sufficient conditions for the proposed method are also presented. Different attack vectors are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection (BDD) with high success rates of up to 95.3%.
In this paper, an attack problem is investigated for event-based remote state estimation in cyber-physical systems. Our objective is to degrade the effect of the event-based scheduler while bypassing a $χ^2$ false data detector. A two-channel scheduler-pointed false data injection attack strategy is proposed by modifying the numerical characteristics of innovation signals. The attack strategy is proved to be always existent, and an algorithm is provided to find it. Under the proposed attack strategy, the scheduler becomes almost invalid and the performance of the remote estimator is degraded. …
False Data Injection (FDI) attacks against powersystem state estimation are a growing concern for operators.Previously, most works on FDI attacks have been performedunder the assumption of the attacker having full knowledge ofthe underlying system without clear justification. In this paper, wedevelop a topology-learning-aided FDI attack that allows stealthycyber-attacks against AC power system state estimation withoutprior knowledge of system information. The attack combinestopology learning technique, based only on branch and bus powerflows, and attacker-side pseudo-residual assessment to per…
University of Windsor（温莎大学）；University of New Brunswick（新不伦瑞克大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；New York Power Authority（纽约电力局）
This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework dynamically detects and classifies false data injection attacks. Then, it retrieves the control signal using the acquired information. This process is accomplished in three main modules, with novel designs, for detection, classification, and control signal retrieval. The detection module monitors historical changes in phasor measurements and captures any deviation pattern caused by an attack on a complex plane. This…
Accurate and reliable dynamic state quantities of generators are very important for real-time monitoring and control of the power system. The emergence of cyber attacks has brought new challenges to the state estimation of generators. Especially, false data injection (FDI) attacks deteriorate the accuracy of state estimation by injecting the false data into the measurement device. In this regard, this paper proposes for the first time an FDI attack model based on the dynamic state estimation of generators. Firstly, Taylor's formula was used to linearize the generator's measurement equation. Se…
The false data injection (FDI) attack is a crucial form of cyber-physical security problems facing cyber-physical power systems. However, there is no research revealing the problem of FDI attacks facing voltage source converter based high voltage direct current transmission (VSC-HVDC) systems. Firstly, the general form of the model of FDI attack strategies is proposed and the essence of the problem of FDI attack strategies is further analyzed. Moreover, the model of FDI attack strategies aiming at disrupting the operation security of converter stations in VSC-HVDC systems is proposed and its s…
Spot pricing is often suggested as a method of increasing demand-side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. In this paper, we consider attacks which aim to alter the consumer load profile to exploit intraday price dips. We examine an anomaly detection protocol for cyber-attacks that seek to leverage spot prices for financial gain. In this way we outline a methodology for detecting attacks on industrial load smart meters. We first create a feature clustering mode…
With the large scale penetration of electric vehicles (EVs) and the advent of bidirectional chargers, EV aggregators will become a major player in the voltage regulation market. This paper proposes a novel false data injection attack (FDIA) against the voltage regulation capacity estimation of EV charging stations, the process that underpins voltage regulation in distribution system. The proposed FDIA takes into account the uncertainty in EV mobility and network conditions. The attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem subject t…
The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operational challenges. This paper investigates the vulnerability of the power grids with high renewable penetration against an intraday false data …
The evolution of the traditional power system towards the modern smart grid has posed many new cybersecurity challenges to this critical infrastructure. One of the most dangerous cybersecurity threats is the False Data Injection (FDI) attack, especially when it is capable of completely bypassing the widely deployed Bad Data Detector of State Estimation and interrupting the normal operation of the power system. Most of the simulated FDI attacks are designed using simplified linearized DC model while most industry standard State Estimation systems are based on the nonlinear AC model. In this pap…
This paper studies the vulnerability of large-scale power systems to false data injection (FDI) attacks through their physical consequences. Prior work has shown that an attacker-defender bi-level linear program (ADBLP) can be used to determine the worst-case consequences of FDI attacks aiming to maximize the physical power flow on a target line. Understanding the consequences of these attacks requires consideration of power system operations commonly used in practice, specifically real-time contingency analysis (RTCA) and security constrained economic dispatch (SCED). An ADBLP is formulated w…
The reliable operation of power grid is supported by energy management systems (EMS) that provide monitoring and control functionalities. Contingency analysis is a critical application of EMS to evaluate the impacts of outages and prepare for system failures. However, false data injection attacks (FDIAs) have demonstrated the possibility of compromising sensor measurements and falsifying the estimated power system states. As a result, FDIAs may mislead system operations and other EMS applications including contingency analysis and optimal power flow. In this paper, we assess the effect of FDIA…
Over the last decade, the number of cyberattacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, False Data Injection Attacks (FDIAs) is a class of cyberattacks against power grid monitoring systems. Adversaries can successfully perform FDIAs in order to manipulate the power system State Estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the Energy Management System (EMS) towards estimating unknown state variables based on system redundant measurements and network topology. SE routines i…
With the increasing use of information and communication technology in electrical power grids, the security of energy supply is increasingly threatened by cyber-attacks. Traditional cyber-security measures, such as firewalls or intrusion detection/prevention systems, can be used as mitigation and prevention measures, but their effective use requires a deep understanding of the potential threat landscape and complex attack processes in energy information systems. Given the complexity and lack of detailed knowledge of coordinated, timed attacks in smart grid applications, we need information and…
We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic. We show that these approaches do not perform well when the attacker has a limited number of data s…
As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grid. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transf…
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 violatio…
Zhejiang University of Technology（浙江工业大学）；Nanyang Technological University（南洋理工大学）；Zhejiang University（浙江大学）
Most traditional false data injection attack (FDIA) detection approaches rely on a key assumption, i.e., the power system can be accurately modeled. However, the transmission line parameters are dynamic and cannot be accurately known during operation and thus the involved modeling errors should not be neglected. In this paper, an illustrative case has revealed that modeling errors in transmission lines significantly weaken the detection effectiveness of conventional FDIA approaches. To tackle this issue, we propose an FDIA detection mechanism from the perspective of transfer learning. Specific…
The advent of smart power grid which plays a vital role in the upcoming smart city era is accompanied with the implementation of a monitoring tool, called state estimation. For the case of the unbalanced residential distribution grid, the state estimating operation which is conducted at a regional scale is considered as an application of the edge computing-based Internet of Things (IoT). While the outcome of the state estimation is important to the subsequent control activities, its accuracy heavily depends on the data integrity of the information collected from the scattered measurement devic…
State estimation is a data processing algorithm for converting redundant meter measurements and other information into an estimate of the state of a power system. Relying heavily on meter measurements, state estimation has proven to be vulnerable to cyber attacks. In this paper, a novel targeted false data injection attack (FDIA) model against AC state estimation is proposed. Leveraging on the intrinsic load dynamics in ambient conditions and important properties of the Ornstein-Uhlenbeck process, we, from the viewpoint of intruders, design an algorithm to extract power network parameters pure…
Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices. One of the key challenges for MTD in power grid is to design its real-time implementation with performance guarantees against unknown attacks. Converting from the noiseless assumptions in the literature, this paper investigates the MTD design problem in a noisy environment and proposes, for the first time, the concept of robust MTD to guarantee the worst-case detection r…
In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Many works consider the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will require first intrusion into a system followed by construction of an attack vector that can bypass bad data detection (BDD). In this way, we develop a full service framework for FDI risk assessment. The framework considers both the costs of system intrusion via a weighted graph asse…
Small-Signal Stability (SSS) is crucial for the control of power grids. However, False Data Injection (FDI) attacks against SSS can impact the grid stability, hence, the security of SSS needs to be studied. This paper proposes a formal method of synthesizing FDI attack vectors (i.e., a set of measurements to be altered) that can destabilize power systems. We formulate an FDI attack as an optimization problem using AC power flow, SSS model, and stability constraints. The attacker capability is modeled as the accessibility to a limited set of measurements. The solution of the proposed FDI attack…
Fast and accurate detection of cyberattacks is a key element for a cyber-resilient power system. Recently, data-driven detectors and physics-based Moving Target Defences (MTD) have been proposed to detect false data injection (FDI) attacks on state estimation. However, the uncontrollable false positive rate of the data-driven detector and the extra cost of frequent MTD usage limit their wide applications. Few works have explored the overlap between these two areas. To fill this gap, this paper proposes blending data-driven and physics-based approaches to enhance the detection performance. To s…
In this paper, we consider a hierarchical control based DC microgrid (DCmG) equipped with unknown input observer (UIO) based detectors, where the potential false data injection (FDI) attacks and the distributed countermeasure are investigated. First, we find that the vulnerability of the UIO-based detector originates from the lacked knowledge of true unknown inputs. Zero trace stealthy (ZTS) attacks can be launched by secretly faking the unknown inputs, under which the detection residual will not be altered, and the impact on the DCmG in terms of voltage balancing and current sharing is theore…
In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to…
Texas A&M University（德克萨斯A&M大学）；Arkansas State University（阿肯色州立大学）；Tennessee Tech University（田纳西理工大学）
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 paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publi…
Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems
Advanced Digital Sciences Center, Illinois at Singapore（伊利诺伊大学新加坡高级数字科学中心）；GovTech Singapore（新加坡政府科技局）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Singapore University of Technology and Design（新加坡国立科技大学）
