Network Parameter Coordinated False Data Injection Attacks Against Power System AC State Estimation

In the recent years, cyberattacks to smart grids are becoming more frequent. Among the many malicious activities that can be launched against smart grids, the False Data Injection (FDI) attacks have raised significant concerns from both academia and industry. FDI attacks can affect the (internal) state estimation process—critical for smart grid monitoring and control—thus being able to bypass conventional Bad Data Detection (BDD) methods. Hence, prompt detection and precise localisation of FDI attacks are becoming of paramount importance to ensure smart grids security and safety. Several papers recently started to study and analyse this topic from different perspectives and address existing challenges. Data‐driven techniques and mathematical modelling are the major ingredients of the proposed approaches. The primary objective is to provide a systematic review and insights into FDI attacks joint detection and localisation approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localisation aspects. For this purpose, more than 40 major research contributions were selected and inspected, while conducting a detailed analysis of the methodology and objectives in relation to the FDI attacks detection and localisation. Key findings of the identified papers were provided according to different criteria, such as employed FDI attacks localisation techniques, utilised evaluation scenarios, investigated FDI attack types, application scenarios, adopted methodologies and the use of additional data. Finally, open issues and future research directions were discussed.

The pervasiveness of information and communications technologies as well as intelligent electronic devices leads to an expanded attack surface in smart grids, making it increasingly challenging to withstand the high-profile false data injection (FDI) attacks. In this paper, we propose a Proactive Approach to Mitigate FDI Attacks (PAMA) in smart grids. With PAMA scheme, the critical information - power grid connections and configurations as well as the original measurement data - used for constructing FDI attacks is well protected from leakage or theft, so that FDI attacks are effectively mitigated. Specifically, we transform the state estimation and FDI detection application into a distributed one equipped with converted information from the critical information provided by the control center. In addition, the original measurement data is also protected by using a secure hybrid Paillier cryptosystem. Our PAMA scheme is proved to be secure and effective in mitigating FDI attacks on smart grids. The computational complexity and the communication overhead are evaluated on the standard IEEE 14-bus test system. Keywords <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">__</sup> Smart grids, false data injection (FDI) attack, Paillier cryptosystem, state estimation.

In this study, to enhance the system reliability under false data injection (FDI) attacks and DC-link voltage (DCLV) sensor failures, a hybrid control strategy for a DC microgrid (DCMG) based on the Lagrange extrapolation and voltage observer is proposed. Under normal conditions without FDI attacks or DCLV sensor failures, the DCMG system works in a distributed control scheme. To enhance the reliability of the system under the DCLV sensor failure or FDI attack, the DCMG system utilizes a hybrid control strategy that combines distributed control with decentralized control. The hybrid control strategy is achieved by the proposed detection algorithms for FDI attacks and DCLV sensor failures. The detection of FDI attacks is accomplished by comparing the predicted secondary controller output based on the Lagrange extrapolation with the actual one. When a power agent detects an FDI attack, its control mode is switched to decentralized control by using the proposed hybrid control strategy. The DCLV sensor failure detection algorithm to enhance system reliability against DCLV sensor failures is achieved by comparing the estimated DCLV with the measured one from the voltage observer. Upon detecting a DCLV sensor failure, the operation of the power agent is switched to the current control mode to sustain the system operation even under DCLV sensor failures. The proposed detection algorithms are simple, effective, and precise, operating without mutual interference that deteriorates the detection accuracy. Simulation and experiments are carried out under various uncertain test conditions to validate the reliability and effectiveness of the proposed control strategy.

False data injection attacks with complete stealthiness in cyber–physical systems: A self-generated approach

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

False data injection (FDI) attacks are malicious insertions of false data as sensor measurements in a cyber-physical system, in order to lead the system to take a wrong action. False data injection attacks do not attack the computational or network components of cyber-physical systems but the interface between the physical and the cyber part. Such attacks are powerful and can have catastrophic results. Defense against them can be achieved by limiting the attack surface through vulnerability analysis of the cyber-physical system design and by monitoring system operation in the field with monitors that observe system parameters and sensor measurements and detect abnormal operation early. In this chapter, we describe promising techniques for vulnerability analysis and dynamic monitoring, based on efficient SMT solvers and Kalman filter techniques, respectively.

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.

In this paper, we consider false data injection (FDI) attacks with limited information of transmission-line susceptances and new countermeasures in smart grids. First, we prove that the adversary could launch FDI attacks to modify the state variable on a bus or superbus only if he/she knows the susceptance of every transmission line that is incident to that bus or superbus. Based on this observation, we provide a new countermeasure against FDI attacks, i.e., to make the susceptances of n-1 interconnected transmission lines that cover all buses unknown to the adversary (e.g., by proactively perturbing transmission-line susceptances through distributed flexible AC transmission system (D-FACTS) devices), where n is the total number of buses. This new countermeasure can work alone or in conjunction with traditional ones to reduce the number of meter measurements/state variables that are to be secured against FDI attacks. The implementation of FDI attacks with limited susceptance information and the effectiveness of new countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.

Design of False Data Injection Attacks in Cyber-Physical Systems

The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids.

This paper studies the secure state estimation problem for multi-area power systems with unknown load deviation when the sensor measurements of power systems encounter false data injection (FDI) attacks. By modeling a new state-space model including the variables of load deviation and FDI attack signals, an intermediate observer-based method is developed to simultaneously estimate the load deviation and FDI attacks. Then, the secure state estimator is designed by compensating FDI attacks, while the unknown load deviation in power systems is compensated by its estimated values to eliminate system oscillations as much as possible. Simulations show the effectiveness of the proposed methods.

ABSTRACTAs a result of the rapid growth of distributed state estimation in modern power systems, power networks are facing increasingly serious security problems, thus requiring the advancement of defense techniques against cyber attacks. This paper is devoted to investigating distributed consensus state estimation with a defense mechanism against false data injection (FDI) attacks for power systems. By introducing a transformation matrix, the local subsystem model associated with the mixed remote terminal unit and phasor measurement unit measurements is constructed. Taking into account historical estimation information on state variables without being attacked, a defense mechanism constructed by secure historical estimation value is presented to protect the estimator against FDI attacks while maintaining accuracy in estimation. Then a distributed Kalman consensus filter (DKCF) is hosted to estimate the dynamic states of power systems under FDI attack protector. Considering scalability in large‐scale power systems, a suboptimal DKCF with the designed protector is developed. By means of the Lyapunov‐based approach, a sufficient condition is provided to ensure that the proposed estimator equipped with the defense mechanism is stable. Finally, the proposed distributed state estimation algorithms are validated on an IEEE benchmark 14‐bus power system.

The cybersecurity of the smart grid is becoming a critical issue to the power systems. Mitigating potential cyberattacks on the control of the power electronic devices is an important concern in the controller design to enhance the resilience of the overall power grid. In this paper, a novel model predictive control (MPC) is proposed for the multilevel cascaded H-bridge inverters considering the false data injection (FDI) attacks. The FDI attack is a vicious attack scenario when the attack signals are injected into the sensors in the cyberphysical systems (CPS). It is shown that FDI signals can effectively penetrate the commonly used Kalman Filter (KF) based state estimation and jeopardize the control of the inverters. The k-Nearest Neighbors (KNN) algorithm is employed in the proposed scheme to mitigate the FDI attacks. The proposed KNN-based MPC scheme was tested with various types of attack signals. The results show that the proposed scheme exhibits a robust performance in the face of the malicious FDI attacks.

The smart grid combines the classical power system with the information technology, leading to a cyber-physical system. In such an environment, the malicious injection of data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect well designed false data injection (FDI) attacks. Moreover, most of the works on FDI attack detection are based on the linearized DC model of the power system and fails to detect attacks based on the AC model. The aim of this paper is to address these problems by using the graph structure of the grid and the AC power flow model. We derive an attack detection method that is able to detect previously undetectable FDI attacks. This method is based on concepts originating from graph signal processing (GSP). The proposed detection scheme calculates the graph Fourier transform of an estimated grid state and filters the graph’s high-frequency components. By comparing the maximum norm of this outcome with a threshold, we can detect the presence of FDI attacks. Case studies on the IEEE 14-bus system demonstrate that the proposed method is able to detect a wide range of previously undetectable attacks, both on angles and on magnitudes of the voltages.

SummaryThe Internet of Things (IoT) has made possible the development of increasingly driven services, like industrial Industrial Internet of Things (IIoT) services, that often deal with massive amounts of data. Meantime, as IIoT networks grow, the threats are even greater, and false data injection (FDI) attacks stand out as being one of the most aggressive. The majority of current solutions to handle this attack do not take into account the data validation, especially on the data clustering service. Aiming to advance on the issue, this work introduces CONsensus Based Data FIlteriNg for IIoT (CONFINIT), an intrusion detection system for mitigating FDI attacks on the data dissemination service performing in dense IIoT networks. CONFINIT combines watchdog surveillance and collaborative consensus strategies for assertively excluding various FDI attacks. The simulations showed that CONFINIT compared with Dynamic Data‐aware Firefly‐based Clustering (DDFC) increased by up to 35%–40% the number of clusters without attackers in a gas pressure IIoT environment. CONFINIT achieved attack detection rates (DRs) of 99%, accuracy of 90, and F1 score of 0.81 in multiple IIoT scenarios, with only up to 3.2% and 3.6% of false negatives and positives rates, respectively. Moreover, under two variants of FDI attacks, called Churn and Sensitive attacks, CONFINIT achieved DRs of 100%, accuracy of 99, and F1 of 0.93 with less than 2% of false positives and negatives rates.