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Communication plays a vital role in incorporating smartness into the interconnected power system. However, historical records prove that the data transfer has always been vulnerable to cyber-attacks. Unless these cyber-attacks are identified and cordoned off, they may lead to black-out and result in national security issues. This paper proposes an optimal two-stage Kalman filter (OTS-KF) for simultaneous state and cyber-attack estimation in automatic generation control (AGC) system. Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics. Five types of cyber-attacks, i.e., false data injection (FDI), data replay attack, denial of service (DoS), scaling, and ramp attacks, are injected into the measurements and estimated using OTS-KF. As the load variations of each area are seldom available, OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system. The proposed technique is validated on the benchmark two-area, three-area, and five-area power system models. The simulation results under various test conditions demonstrate the efficacy of the proposed filter.

Stealthy attacks in sensor and actuator loops are the research priorities in the security of cyber-physical systems. Existing attacks define the stealthiness conditions against the Chi-square or Kullback-Leibler divergence detectors and parameterize the attack model based on additive signals. Such conditions ignore the potential anomalies of the vulnerable outputs in the control layer, and the attack sequences need to be generated online, increasing the hardware and software costs. This paper investigates a type of multiplicative attack with essential stealthiness where the employed model is a novel form. The advantage is that the parameters can be designed in a constant form without having to be generated online. An essential stealthiness condition is proposed for the first time and complements the existing ones. Two sufficient conditions for the existence of constant attack matrices are given in the form of theorems, where two methods for decoupling the unknown variables are particularly considered. A quadruple-tank process, an experimental platform for attack and defense, is developed to verify the theoretical results. The experiments indicate that the proposed attack strategy can fulfill both the attack performance and stealthiness conditions.

Smart grids are one of the most important structures based on cyber–physical devices. Therefore, these grids should be more reliable and efficient when increasing consumption. In recent years, however, with the automation of power grids and data transferring in a telecommunications infrastructure, the possibility of planning cyber-attacks has become a significant threat to systems integrity. Well-planned cyber-attacks can have catastrophic effects on the system to the extent that they may lead to a global blackout. It has been proven that, the optimization of the budgets for attack and defense in a power grid through cyber-attack is so essential. The aim of the present paper is investigate and simulate the most vulnerable operating bus using the state estimation technique through a new algorithm. The false data injection is performed on the data sent from the Phasor Measurement Units in such a way that it is not detectable to the grid dispatching operator under the invasion. In other words, when a cyber-attack occurs, the defender does not notice the attack and thus the estimated modes are destroyed. The proposed algorithm is implemented on a 14 IEEE bus network, and the best bus in terms of exposure under attack is identified. The proposed algorithm is based on the results obtained from state estimating of the grid after occur a cyber-attack on different buses. It was found that, the best bus to carry the attack was bus number 5, which causes the most changes in state variables with the least measurement damage. In addition, the most damage to the grid occurs when a cyber-attack causes the most damage in estimating the state of the most grid state variables.

Computing devices are everywhere, controlling systems of different scales, from drones of the size of a coin to country-wise energy distribution grids. The amalgamation of these devices and the physical systems essential for our everyday lives originates the cyber-physical systems. Depending on their scale, the computation is divided into multiple computing devices, which need to negotiate to find a consensus of the best control to be applied. The communication among agents is vital for the consensus to be achieved. However, the negotiation is sensitive to deviations in the exchanged values. An attacker can use this information to influence the negotiation to benefit a group of agents. This thesis proposes a study from the conception of the control of such systems to its security. And then, we propose methods to secure the system from attacks where an ill-intentioned agent injects false data into the negotiation. The performance of the proposed methods is assessed through academic simulations of a small district heating network.; Les dispositifs informatiques sont partout, ils commandent systèmes de différentes échelles, dès drones de la taille d'une pièce aux systèmes de distribution d'énergie de la taille des pays. La mixture de ces dispositifs avec des systèmes physiques essentiels pour la vie quotidienne origine les systèmes cyber-physiques. Dépendant de l'échelle des systèmes, la computation est divisée en différents dispositifs, qui nécessitent de négocier pour trouver un consensus de la meilleure commande à être appliquée. La communication entre agents est d'extrême importance pour que le consensus soit achevé. Par contre, la négociation est sensible à déviations dans les valeurs échangées. An attaquant peut utiliser cette information pour influencer la négociation pour bénéficier un groupe d'agents. Cette thèse propose une étude de la conception de la commande de tels systèmes jusqu'à leur sécurité. Et après, on propose des méthodes pour sécuriser le système contre des attaques où l'agent malintentionné injecte de la fausse information pendant la négociation. La performance des méthodes est étudiée en utilisant des simulations d'un petit réseau de distribution de chaleur.

Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber–physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.

Given their crucial role for a society and economy, an essential component of critical infrastructures is the Bad State Estimator (BSE), responsible for detecting malfunctions affecting elements of the physical infrastructure. In the past, the BSE has been conceived to mainly cope with accidental faults, under assumptions characterizing their occurrence. However, evolution of the addressed systems category consisting in pervasiveness of ICT-based control towards increasing smartness, paired with the openness of the operational environment, contributed to expose critical infrastructures to intentional attacks, e.g. exploited through False Data Injection (FDI). In the flow of studies focusing on enhancements of the traditional BSE to account for FDI attacks, this paper proposes a new solution that introduces randomness elements in the diagnosis process, to improve detection abilities and mitigate potentially catastrophic common-mode errors. Differently from existing alternatives, the strength of this new technique is that it does not require any additional components or alternative source of information with respect to the classic BSE. Numerical experiments conducted on two IEEE transmission grid tests, taken as representative use cases, show the applicability and benefits of the new solution.

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 approach can help to reveal characteristics of the attacks including the direction, magnitude, and ratio of the injected false data. Using this information, the signal retrieval module can easily recover the original control signal and remove the injected false data. Further information regarding the attack type can be obtained through the classifier module. The proposed ensemble learner is compatible with harsh learning conditions including the lack of labeled data, concept drift, concept evolution, recurring classes, and independence from external updates. The proposed novel classifier can dynamically learn from data and classify attacks under all these harsh learning conditions. The introduced framework is evaluated w.r.t. real-world data captured from the Central New York Power System. The obtained results indicate the efficacy and stability of the proposed framework., Comment: Accepted for publication in IEEE Transactions on Information Forensics and Security

The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly vulnerable to being compromised under false data injection attacks (FDIAs). The problem is that the majority of existing CDFS employ anomaly-based intrusion detection systems (AIDS) to combat FDIAs. Since the distribution of demand time series keeps changing naturally with time, AIDS treat a major change in the distribution as an attack, but this approach is not effective against colluding FDIAs. To overcome this problem, we propose a novel resilient CDFS called PRDFS (Proposed Resilient Demand Forecasting System). The primary novelty of PRDFS is that it uses signature-based intrusion detection systems (SIDS) that automatically generate attack signatures through the game-theoretic approach for the early detection of malicious nodes. We simulate the performance of PRDFS under colluding FDIA on High Performance Computing (HPC). The simulation results show that the demand forecasting resilience of PRDFS never goes below 80%, regardless of the percentage of malicious nodes. In contrast, the resilience of the benchmark system decreases sharply from about 99% to less than 30%, over the simulation period as the percentage of malicious nodes increases.

Today, robotic vehicles (RV) play an essential role in both modern industry and the military. More and more RVs become autonomously operated and are prone to cyber-attacks of their embedded control systems. These attacks can be detected and prevented with the appropriate tools but can also be almost undetectable if executed correctly. In this work, a type of cyber-attack, namely false data injection is performed to spoof an Extended Kalman Filter (EKF) and a conventional fault detection mechanism is used to attempt to identify the attack. A two-wheeled differentially steered robotic vehicle based on the TurtleBot 3 ‘Burger’ is simulated in MATLAB. The navigation system is attacked by injecting false data into the measurement device - a LiDAR sensor. The false data injection alters the range and bearing measurements and aims to change the measured heading angle of the robot and alter its trajectory. The attack is then tuned to make it undetectable by the implemented counter measures. In this thesis, it is demonstrated that false data injection can be executed in such a way that it is almost impossible for the fault detection mechanism to perceive an attack. The simulations have shown that the key to stealth is to inject the false data in a slow and steady manner to deceive the EKF by steering its output. The deviation introduced is mostly unnoticeable as long as it is in the same order of magnitude of the noise, which is part of the robot’s navigation system. In contrast, sudden and severe changes of measurement data are detected easily by the EKF and the fault detection mechanism. Universitaet der Bundeswehr Muenchen Fakultaet fuer Luft- und Raumfahrttechnik Institute for Space Technology and Applications Werner-Heisenberg-Weg 39 85579 Neubiberg, Germany German Armed Forces Approved for public release; distribution is unlimited.

Supervisory Control and Data Acquisition (SCADA) systems are essential for reliable communication and control of smart grids. However, in the cyber-physical realm, it becomes highly vulnerable to cyber-attacks like False Data Injection (FDI) into the measurement signal which can circumvent the conventional detection methods and interfere with the normal operation of grids, which in turn could potentially lead to huge financial losses and can have a large impact on public safety. It is imperative to have an accurate state estimation of power consumption for further operational decision-making.This work presents novel forecasting-aided anomaly detection using an CNN-LSTM based auto-encoder sequence to sequence architecture to combat against false data injection attacks. We further present an adaptive optimal threshold based on the consumption patterns to identify abnormal behaviour. Evaluation is performed on real-time energy demand consumption data collected from the Australian Energy Market Operator. An extensive experiment shows that the proposed model outperforms other benchmark algorithms in not only improving the data injection attack (95.43%) but also significantly reducing the false positive rate.

This paper presents the artificial intelligence (AI) techniques based on the deep learning algorithms to diagnose false data injection (FDI) attacks to smart grids with the measurement data in real-time. The power and data flow between end-user consumers and all components of the advanced metering infrastructure (AMI) and supervisory control and data acquisition (SCADA) system in the SG is bidirectional flow by advanced communication networks. For all the advantages of the SG come with, they remain at risk to a series of many potential threats and ongoing attacks. The conditional-deep-belief-network (CDBN) architecture is employed to un-observable FDI attacks which pass the state-vector-estimator (SVE) mechanisms. The IEEE 118 bus, and IEEE 300 bus power system have been used to evaluate our detection scheme. Finally, the suggested CDBN scheme is compared with other detection such as artificial neural network (ANN) and support vector machine (SVM). It is observed that the simulation result shows that suggested detection methods can attain a high accuracy of unobservable FDI attacks.

Today’s energy resources are closer to consumers thanks to sustainable energy and advanced metering infrastructure (AMI), such as smart meters. Smart meters are controlled and manipulated through various interfaces in smart grids, such as cyber, physical and social interfaces. Recently, a large number of non-technical losses (NTLs) have been reported in smart grids worldwide. These are partially caused by false data injections (FDIs). Therefore, ensuring a secure communication medium and protected AMIs is critical to ensuring reliable power supply to consumers. In this paper, we propose a novel Big Data-driven solution that employs machine learning, deep learning and parallel computing techniques. We additionally obtained robust statistical features to detect the FDIs based cyber threats at the distribution level. The performance of the proposed model for NTL detection is investigated using private smart grid datasets in the Turkish distribution network for AMI-level cyber threats, and the results are compared to state-of-the-art machine learning algorithms used for NTL classification problems. Our approach shows promising results, as the accuracy, specificity, and precision metrics of most classifiers are above 90% and false positive rates vary between 0.005 to 0.027.

Recent advances in the cyber-physical smart grid (CPSG) have enabled a broad range of new devices based on the information and communication technology (ICT). However, these ICT-enabled devices are susceptible to a growing threat of cyber-physical attacks. This paper performs a thorough review of the state-of-the-art cyber-physical security of the smart grid. By focusing on the physical layer of the CPSG, this paper provides an abstracted and unified state-space model, in which cyber-physical attack and defense models can be effectively generalized. The existing cyber-physical attacks are categorized in terms of their target components. We then discuss several operational and informational defense approaches that present the current state-of-the-art in the field, including moving target defense, watermarking, and data-driven approaches. Finally, we discuss challenges and future opportunities associated with the smart grid cyber-physical security.

Due to recent increase in deployment of Cyber-Physical Industrial Control Systems in different critical infrastructures, addressing cyber-security challenges of these systems is vital for assuring their reliability and secure operation in presence of malicious cyber attacks. Towards this end, developing a testbed to generate real-time data-sets for critical infrastructure that would be utilized for validation of real-time attack detection algorithms are indeed highly needed. This paper investigates and proposes the design and implementation of a cyber-physical industrial control system testbed where the Tennessee Eastman process is simulated in real-time on a PC and the closed-loop controllers are implemented on the Siemens PLCs. False data injection cyber attacks are injected to the developed testbed through the man-in-the-middle structure where the malicious hackers can in real-time modify the sensor measurements that are sent to the PLCs. Furthermore, various cyber attack detection algorithms are developed and implemented in real-time on the testbed and their performance and capabilities are compared and evaluated. North Atlantic Treaty Organization; Qatar Foundation; Qatar National Research Fund Scopus

With the evolution of the Internet of Things (IoT), various types of devices and massive systems comprising national infrastructures such as a smart grid are connected on a network, which poses various types of security issues in a cyber physical system. In this paper, we propose two false data injection attacks, which are on the forward path and the feedback path of a control system. Both are designed with a controllable parameter which determines the degree of degradation. A defensive method of inversing a linear forward attack through estimating with least square or minimum mean squared method was developed. A conventional Kalman filter was considered as a defensive method for a noise injection attack on the feedback path. The numerical evaluation verifies that the parameters of the proposed attacks control the degree of performance degradation of the control system, and the proposed defenses can effectively defend the proposed attacks.

In order to enable many of the required smart grid functionalities, distribution systems are becoming increasingly dependent on state estimators. Many cyber-attacks attempt false data injection (FDI) attacks on such state estimators. The majority of the existing literature deal with FDIs in distribution systems state estimation either by the analysis of the residual vector elements, or by the analysis of historical data. In this work, we adopt an alternative approach for the detection of FDIs in distribution system state estimation, wherein FDIs are modelled as measurement biases and a bias filter is employed for FDI detection. Additionally, in order to enable the detection of time-variable FDIs, a failure detector is integrated in the recursive formulation of the bias filter, which is based on the Kalman filter. The developed approach is accordingly capable of identifying time-varying FDIs, which can evade many of the existing FDI detection methods. Simulation case studies are performed on the IEEE 13-node and 123-node feeders with different FDIs and the performance of the proposed approach is analyzed.

The concept of false data injection attack (FDIA) was introduced originally in the smart grid domain. While the term sounds common, it specifically means the case when an attacker compromises sensor readings in such tricky way that undetected errors are introduced into calculations of state variables and values. Due to the rapid growth of the Internet and associated complex adaptive systems, cyber attackers are interested in exploiting similar attacks in other application domains such as healthcare, finance, defense, governance, etc. In today’s increasingly perilous cyber world of complex adaptive systems, FDIA has become one of the top-priority issues to deal with. It is a necessity today for greater awareness and better mechanism to counter such attack in the cyberspace. Hence, this work presents an overview of the attack, identifies the impact of FDIA in critical domains, and talks about the countermeasures. A taxonomy of the existing countermeasures to defend against FDIA is provided. Unlike other works, we propose some evaluation metrics for FDIA detection and also highlight the scarcity of benchmark datasets to validate the performance of FDIA detection techniques.

Because charging coordination is a solution for avoiding grid instability by prioritizing charging requests, electric vehicles may lie and send false data to illegally receive higher charging priorities. In this article, we first study the impact of such attacks on both the lying and honest electric vehicles. Our evaluations indicate that lying electric vehicles have a higher chance of charging, whereas honest electric vehicles may not be able to charge or may charge late. Then, an anomaly-based detector based on a deep neural network is devised to identify lying electric vehicles. The idea is that since each electric vehicle driver has a particular driving pattern, the data reported by the corresponding electric vehicle should follow this pattern, and any deviation due to reporting false data can be detected. To train the detector, we first create an honest dataset for the charging coordination application using real driving traces and information provided by an electric vehicle manufacturer, and we then propose a number of attacks as a basis for creating malicious data. We train and evaluate a gated recurrent unit model using this dataset. Our evaluations indicate that our detector can detect lying electric vehicles with high accuracy and a low false alarm rate even when tested on attacks that are not represented in the training dataset.

This letter develops a new bilevel optimization model to construct false data injection attack based on pre- and post- dispatch. In order to enhance the attack concealment, the proposed bilevel model can minimize the variation of uploaded measurements between pre- and post-attack before dispatching, after which the attack can lead the system to an uneconomic and insecure operating state after dispatching. Simulation results validate the effectiveness of the proposed bilevel model in term of operating cost and network overloads.

Expanding adoption of electric vehicles (EVs) and broad deployment of charging stations push the limits of distribution grid infrastructure and increase the importance of effective charging coordination. Smart EV chargers with several functionalities and charging coordination solutions that can manage the charging sessions of hundreds of EVs are becoming common, with the increasing risk of triggering significant operational problems in case of cyberattacks. The information exchange between the charging coordinator, distribution network operator, and users is essential in the scheduling of a large number of charging sessions, relying on customer preferences, without violating operational grid constraints. Both the user mobile apps used for charging session reservations and DSO-charging coordinator interfaces are vulnerable to cyberattacks which may cause considerable technical and economic consequences. An important concern is the potential impacts of attacks when a single node or communication link is compromised. This study investigates the impacts of false data injection (FDI) and hijacking attacks on EV charging coordination in case of a single point of failure. Hijacking of one user\"s mobile app and FDI attack on the DSO-charging coordinator interface are investigated by simulating a 24-hour scenario with 12 chargers, 34 realistic charging sessions, and an EV charging coordination approach based on each session\"s tolerance to delays. The study highlighted considerable negative impacts that could be encountered in case of a single point of failure in EV charging coordination.

Cyber-physical systems (CPSs) are vulnerable to cyber-attacks. Nowadays, the detection of cyber-attacks in microgrids as examples of CPS has become an important topic due to their wide use in various practical applications from renewable energy plants to power distribution and electric transportation. In this article, we propose a new artificial intelligence (AI)-based method for the detection of cyber-attacks in direct current (dc) microgrids and also the identification of the attacked distributed energy resource (DER) unit. The proposed method works based on the time-series analysis and a nonlinear auto-regressive exogenous model (NARX) neural network, which is a special type of recurrent neural network for estimating dc voltages and currents. In the proposed method, we consider the effect of cyber-attacks named false data injection attacks (FDIAs), which try to affect the accurate voltage regulation and current sharing by affecting voltage and current sensors. In the presented strategy, first, a dc microgrid is operated and controlled without any FDIAs to gather enough data during the normal operation required for the training of NARX neural networks. It is worth mentioning that in the data generation process, load changing is also considered to have distinguishing data sets for load changing and cyber-attack scenarios. Trained and fine-tuned NARX neural networks are exploited in an online manner to estimate the output dc voltages and currents of DER units in dc microgrid. Then, based on the error of estimation, the cyber-attack is detected. To show the effectiveness of the proposed method, offline digital time-domain simulation studies are performed on a test dc microgrid system in the MATLAB/Simulink environment, and the results are verified using real-time simulations using the OPAL-RT real-time digital simulator (RTDS).

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

If a smart grid is to be described in one word, that word would be ’connectivity’. While electricity production and consumption still depend on a limited number of physical connections, exchanging data is growing enormously. Customers, utilities, sensors, and markets are all different sources of data that are exchanged in a ubiquitous digital setup. To deal with data complexity, many researchers recently focused on machine learning (ML) applications in smart grids. Much of the success in ML is attributed to discriminative learning where models define boundaries to categorize data. Generative learning, however, reveals how data is generated by learning the underlying distribution functions. In the past few years, generative models brought new dimensions to various domains. Computers became painters and composers. This thesis identifies three applications in the smart grid where generative learning has great potential. On the demand side, residential loads such as dishwashers and clothes driers are simulated using generative models. In specific, the latest developments in generative adversarial networks and kernel density estimators are levered to learn the underlying distributions of individual loads for both power consumption patterns and usage habits. Being data-driven, the learning process eliminates any biases introduced by rule-based models where predetermined fixed formulas describing each load are considered. The study demonstrates the flexibility, viability, and remarkable accuracy of the proposed framework. The resulting synthetic power consumption patterns and usage habits for individual loads are valuable sources for researchers to build or improve their data-driven models for demand-side studies. Non-intrusive load monitoring (NILM) is the second topic researched on the demand side. The goal in NILM is to identify the status of individual loads in a household by merely relying on a smart meter’s measurements without any hardware installations. The research focuses on identifying the operational condition of individual loads by developing a novel hybrid algorithm that combines the widely used generative technique, namely, hidden Markov model, with k-means clustering. The hybrid model is demonstrated to accurately identify the operation conditions of individual loads based on the ingested aggregate signal. Finally, for power transmission, a combination of generative models is proposed to estimate power states from a set of redundant measurements. Power state estimation is a fundamental technique in shedding light on the operational condition of the grid. A traditional state estimator is typically executed online and, in its non-linear formulation, involves a high level of computational complexity. Generative models shift that burden to the offline learning process. On the other hand, bad data detection and identification is a central feature in traditional estimators. As such, the developed framework integrated that feature in the data-driven state estimator by incorporating forward and backward generative adversarial networks. Simple domain knowledge is further incorporated in the model to improve its accuracy against the benchmark data-driven model. The proposed framework remarkably detected tampered measurements including false data injection.

Accurate and reliable estimation of generator's dynamic state vectors in real time are critical to the monitoring and control of power systems. A robust Cubature Kalman Filter (RCKF) based approach is proposed for dynamic state estimation (DSE) of generators under cyber attacks in this paper. First, two types of cyber attacks, namely false data injection and denial of service attacks, are modelled and thereby introduced into DSE of a generator by mixing the attack vectors with the measurement data; Second, under cyber attacks with different degrees of sophistication, the RCKF algorithm and the Cubature Kalman Filter (CKF) algorithm are adopted to the DSE, and then the two algorithms are compared and discussed. The novelty of this study lies primarily in our attempt to introduce cyber attacks into DSE of generators. The simulation results on the IEEE 9-bus system and the New England 16-machine 68-bus system verify the effectiveness and superiority of the RCKF., Comment: Accepted by IEEE Access

In this paper, we extend our previous research on uniting the Cognitive Dynamic Systems (CDS) and the Smart Grid (SG) by introducing Cognitive Risk Control (CRC). The CDS is a structured physical model and research tool inspired by certain features of the brain. The CRC is an advanced feature of the CDS that embodies the concept of predictive adaptation allowing it to bring risk under control in situations involving unexpected or abnormal uncertainty such as a cyber-attack. The False Data Injection (FDI) attack is a special class of cyber-attack targeting the SG that is able to bypass the traditional bad data detection techniques. Here we will demonstrate how the entropic state, which is the objective function of the CDS, is able to detect and bring FDI attacks under control under the action of CRC. Through Task-Switch control, the CDS is able to switch on a new executive with different set of actions that affects the system configuration to bring the risk under control during an attack. With the CDS acting as the supervisor of the SG, simulations are carried out on a 4 bus-system and IEEE 14-bus system to demonstrate the capability of CRC when faced with FDI attacks. The results show that this system has great potential for future SG systems.

With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. In this paper, an attacker is considered a participant in the virtual bidding procedure in the day-ahead (DA) and real-time (RT) electricity markets to maximize its profit. The cyber attacker attempts to identify the optimal power system measurements to attack along with the false data injected into measurement devices. Towards the maximum profit, the attacker needs to specify the relation between manipulated meters, virtual power traded in the markets, and electricity prices. Meanwhile, to avoid being detected by the system operator, the attacker considers the physical power system constraints existing in the DA and RT markets. Then, a bi-level optimization model is presented which combines the real electricity market state variables with the attacker decision-variables. Using the mathematical problem with equilibrium constraints, the presented bi-level model is converted into a single level optimization problem and the optimal decision variables for the attacker are obtained. Finally, simulation results are provided to demonstrate the performance of the attacker, which also provides insights for security improvement.

This paper introduces a new way of thinking that characterizes itself by uniting two entities, namely state estimation in the smart grid (SG) and cognitive dynamic system (CDS). False data injection (FDI) attacks are a family of new attacks that have been considered to be the most dangerous cyber-attack as it leads to cascaded bad decision making throughout the SG network, which can lead to severe repercussions. The conventional state estimation and bad data detection techniques, which have been applied to reduce observation errors and detect bad data in energy system state estimators, cannot detect FDI attacks. Here, we bring into play an objective-seeking system to act as the supervisor of the SG network. To this end, we propose to introduce a new metric for the SG: the entropic state. The entropic state has two purposes: 1) it provides an indication of the grid's health on a cycle-to-cycle basis and 2) it can be used to detect FDI attacks. Consequently, improving the entropic state is the goal of the supervisor. To achieve that objective, the supervisor dynamically optimizes the state estimation process by reconfiguring the weights of the sensors in the network. With optimality in mind, the CDS is the superior choice for the supervisory system. In this structure, the CDS interacts with the SG network, which is considered as the environment. Computer simulations are carried out on a 4-bus and the IEEE 14-bus systems to highlight the performance of the proposed approach in detecting both bad data and FDI attacks in the SG, respectively.

The traditional power systems are rapidly digitalized and automated for increased monitoring and control. This automation of power system communication has made it possible to monitor and control operations remotely in a plant. However, this also opens up an exploitation vector for attackers, after they gain access to the substation network. This scenario can only be investigated through an in-depth study of communication protocols and control authority concepts associated with power system. IEC 61850 has emerged as the most popular protocol for power system communication. In this paper, we investigate realtime simulation of power systems with IEC 61850 based communication, in order to devise a testbed that can be used to validate false data injection cyberattacks and evaluate their impact. Based on the results, we discuss possible countermeasures to such attacks and outline future research directions. Scopus

Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.

With the rapid development of the smart grid and increasingly integrated communication networks, power grids are facing serious cyber-security problems. This paper reviews existing studies on the impact of false data injection attacks on power systems from three aspects. First, false data injection can adversely affect economic dispatch by increasing the operational cost of the power system or causing sequential overloads and even outages. Second, attackers can inject false data to the power system state estimator, and this will prevent the operators from obtaining the true operating conditions of the system. Third, false data injection attacks can degrade the distributed control of distributed generators or microgrids inducing a power imbalance between supply and demand. This paper fully covers the potential vulnerabilities of power systems to cyber-attacks to help system operators understand the system vulnerability and take effective countermeasures.

The smart grid system is one of the key infrastructures required to sustain our future society. It is a complex system that comprises two independent parts: power grids and communication networks. There have been several cyber attacks on smart grid systems in recent years that have caused significant consequences. Therefore, cybersecurity training specific to the smart grid system is essential in order to handle these security issues adequately. Unfortunately, concepts related to automation, ICT, smart grids, and other physical sectors are typically not covered by conventional training and education methods. These cybersecurity experiences can be achieved by conducting training using a smart grid co-simulation, which is the integration of at least two simulation models. However, there has been little effort to research attack simulation tools for smart grids. In this research, we first review the existing research in the field, and then propose a smart grid attack co-simulation framework called GridAttackSim based on the combination of GridLAB-D, ns-3, and FNCS. The proposed architecture allows us to simulate smart grid infrastructure features with various cybersecurity attacks and then visualize their consequences automatically. Furthermore, the simulator not only features a set of built-in attack profiles but also enables scientists and electric utilities interested in improving smart grid security to design new ones. Case studies were conducted to validate the key functionalities of the proposed framework. The simulation results are supported by relevant works in the field, and the system can potentially be deployed for cybersecurity training and research.

The smart grid is forecasted to be the future of the grid by integrating the traditional grid with information and communication technology. However, the use of this technology has not only brought its benefits but also the vulnerability to cyber-attacks. False data injection (FDI) attacks are a new category of attacks targeting the smart grid that manipulates the state estimation process to trigger a chain of incorrect control decisions leading to severe impacts. This research proposes the use of cognitive dynamic systems (CDS) to address the cyber-security issue and improve state estimation. CDS is a powerful research tool inspired by certain features of the brain that can be used to study complex systems. As two of its special features, Cognitive Control (CC) is concerned with control in the absence of uncertainty, Cognitive Risk Control (CRC) uses the concept of predictive adaptation to bring risk under control in the presence of unexpected uncertainty. The primary research objective of this thesis is to apply the CDS for the SG with emphasis on state estimation and cyber-security. The main objective of CC is to improve the state estimation process while CRC is concerned with mitigating cyber-attacks. Simulation results show that the proposed methods have robust performance for both state estimation and cyber-attack mitigation under various challenging scenarios. This thesis contributes to the body of knowledge by achieving the following objectives: proposes the first theoretical work that integrates the CDS with the DC model of the SG for control and cyber-attack detection; demonstrates the first experimental work that brings a new concept of CRC for cyber-attack mitigation for the DC state estimator; introduces a new CDS architecture adapted for the AC model of the SG for state estimation and cyber-attack mitigation which builds upon all the research efforts made previously. Thesis Doctor of Philosophy (PhD) The smart grid is forecasted to be the future of the grid by integrating the traditional grid with information and communication technology. However, the use of this technology has not only brought its benefits but also the vulnerability to cyber-attacks. False data injection attacks is a new category of attacks targeting the smart grid that can cause serious damage by manipulating the state estimation process and starting a chain of incorrect control decisions. The cognitive dynamic system is a powerful research tool inspired by the brain that can be used to study real time cyber physical systems. The key goal of this thesis is to apply cognitive dynamic systems to the smart grid to improve the state estimation process, detect cyber-attacks and mitigate their effects. Simulation results show that the proposed methods have robust performance in both state estimation and cyber-attack mitigation under various challenging scenarios.

Inverter-based energy resource is a fast emerging technology for microgrids. Operation of micorgrids with integration of these resources, especially in an islanded operation mode, is challenging. To effectively capture microgrid dynamics and also control these resources in islanded microgrids, a heavy cyber and communication infrastructure is required. This high reliance of microgrids on cyber interfaces makes these systems prone to cyber-disruptions. Hence, the hierarchical control of microgrids, including primary, secondary, and tertiary control, needs to be developed to operate resiliently. This paper shows the vulnerability of microgrid control in the presence of False Data Injection (FDI) attack, which is one type of cyber-disruption. Then, this paper focuses on designing a resilient secondary control based on Unknown Input Observer (UIO) against FDI. The simulation results show the superior performance of the proposed controller over other standard controllers.

This paper addresses false data injection, which is one of the most significant security challenges in smart grids. Having an accurately estimated state is of great importance for maintaining a stable running condition of smart grids. To preserve the accuracy of the estimated state, bad data detection (BDD) mechanisms are utilized to remove erroneous measurements due to meter failures or outsider attacks. In this paper we use a graph-theoretical formulation for false data injection attacks in smart grids and propose defense mechanisms to mitigating this type of attacks. To this end we discuss characteristics of a typical smart grid graph such as planarity. Then we propose three different approaches for finding optimal protected meters set: a fast and efficient heuristic algorithm that works well in practice, an approximation algorithm that provides guarantee for the quality of the protected set, and an exact algorithm that find the optimal solution. Our extensive simulation results show that our algorithms outperform similar existing solutions in terms of different performance metrics.

We study low-latency detections of sparse false data injection attacks in power grids, where an adversary can maliciously manipulate power grid operations by modifying measurements at a small number of smart meters. When a power grid is under attack, the detection delay, which is defined as the time difference between the occurrence and detection of the attack, is critical to power grid operations. A shorter detection delay can shorten the response time, thus prevent catastrophic impacts from the attack. The objective of this paper is to develop low-latency false data detection algorithms that can minimize the detection delay subject to constraints on false alarm probability. The false data injection can be modeled with a sparse attack vector, with each non-zero element corresponding to one meter under attack. Since neither the support nor the values of the sparse attack vector is known, a new orthogonal matching pursuit cumulative sum (OMP-CUSUM) algorithm is proposed to identify the meters under attack while minimizing the detection delay. In order to recover the support of the sparse vector, we develop a new stopping condition for the iterative OMP algorithm by analyzing the statistical properties of the power grid measurements. Theoretical analysis and simulation results show that the proposed OMP-CUSUM algorithm can efficiently identify the meters under attack, and reliably detect false data injections with low delays while maintaining good detection accuracy.

False data injection (FDI) attack is the most common data integrity attack, and it is also one of the most serious threats in industrial control systems (ICSs). Although many detection approaches are developed with burgeoning research interests, the technical capability of existing detection methods is still insufficient because the stealth FDI attacks have been proven to bypass bad data detector. In this paper, a novel data analytical algorithm is proposed to identify the stealth FDI attacks in ICSs according to the correlation analysis. First, we evaluate the correlation between measurements and control variables based on an improved grey relational analysis. Then, SVM is used to classify the FDI attack according to the values of correlation. Through a reliable semi-physical simulation testbed whose virtual plant corresponds to a 330 MW boiler-turbine unit, two FDI attacks that can bypass the detection system are studied. A dataset, which contains the normal data and attack data, is created from the testbed to verify the effectiveness of the proposed algorithm. In addition, the performance of the proposed algorithm is also studied based on the new gas pipeline dataset that is collected by the distributed analytics and security institute in Mississippi State University. Such a novel algorithm, which has better accuracy and reliability, is compared with the state of the art based on the data analysis.

In this paper, optimal attack schemes against load frequency control (LFC) are studied by considering coordinated attack. By attacking sensor measurement (load) simultaneously, the attacker disrupts the normal operation of LFC, thus causing excess system frequency/generation excursions. From the perspective of the attacker, LFC system information availability is essential to the attack scheme design. In this paper, two scenarios, where LFC system information is and is not available to the attacker, are considered and optimal attack schemes are studied for these two scenarios, respectively. The optimal attack scheme design is modeled by an optimization problem with different objectives (attack goals): 1) maximization of frequency/generation disruption and 2) least-effort attack (minimization of attack cost). In consideration of counterattack, a threshold-based detection method is used to detect the optimal attack schemes. Two case studies, corresponding to these two scenarios, are simulated, and the results show that attack performances vary with different attack goals and the detection approach can screen out the compromised signals.

The evolution of traditional energy networks toward smart grids increases security vulnerabilities in the power system infrastructure. State estimation plays an essential role in the effficient and reliable operation of power systems, so its security is a major concern. Coordinated cyber-attacks, including false data injection (FDI) attack, can manipulate smart meters to present serious threats to grid operations. In this paper, a robust state estimation algorithm against FDI attack is presented. As a solution to mitigate such an attack, a new analytical technique is proposed based on the Markov chain theory and Euclidean distance metric. Using historical data of a set of trusted buses, a Markov chain model of the system normal operation is formulated. The estimated states are analyzed by calculating the Euclidean distance from the Markov model. States, which match the lower probability, are considered as attacked states. It is shown that the proposed method is able to detect malicious attack, which is undetectable by traditional bad data detection (BDD) methods. The proposed robust dynamic state estimation algorithm is built on a Kalman filter, and implemented on the massively parallel architecture of graphic processing unit using fine-grained parallel programming techniques. Numerical simulations demonstrate the efficiency and accuracy of the proposed mechanism.

The growth of renewable energy generation in the power grid brings attention to high-voltage direct current (HVDC) transmission as a valuable solution for stabilizing the system. Robust hybrid power system state estimation could enhance the resilience of the control of these systems. This paper proposes a two-stage, highly robust least-trimmed squares (LTS)-based estimator. The first step combines the supervisory control and data acquisition (SCADA) measurements using the robust LTS-based estimator. The second step merges the obtained state results with the available phasor measurement units (PMUs) measurements using a robust Huber M-estimator. The proposed robust LTS-based estimator shows good performance in the presence of Gaussian measurement noise. The proposed estimator is shown to resist and correct the effect of false data injection (FDI) attacks and random errors on the measurement vector and the Jacobian matrix. The state estimation (SE) is executed on a modified version of the CIGRE bipole LCC-HVDC benchmark model integrated into the IEEE 12-bus AC dynamic test system. The obtained simulation results confirm the effectiveness and robustness of the proposed two-stage LTS-based SE.

False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection.

In this paper, we investigate the process of detection of False Data Injection (FDI) in a Linear Parameter Varying (LPV) cyber-physical system (CPS). We design a model based FDI detector capable of detecting false data injections on output measurements and scheduling variables. To improve the detection accuracy of FDI attacks, the attack detector design uses the performance metric H- to maximize the detection capability of the detector module to effectively detect FDI attacks. On the other hand, it uses the H? metric to minimize the effect of disturbance on the detector module given an unreliable network. We assume that the network unreliability comes from packet dropout that we modeled as Bernoulli process. The FDI attack detector is designed such that H- and H? performance metrics are maintained despite packet dropout. Based on stochastic stability, we define a set of sufficient Linear Matrix Inequalities (LMI) that we solve as a multi-objective optimization problem to obtain the detector gain. The obtained detector gain is used for estimating the current system state and current output measurement using the system input, manipulated measurements and manipulated scheduling variables. Then, the output of the detector is compared with the actual sensor measurement. The resulting residual signal carries the information about the FDI attack. The proposed approach is tested and validated on a two-tank system. The evaluation results demonstrate that the proposed detector is able to detect FDI attacks. - 2019 IEEE. This work was made possible by NPRP grant # NPRP9-005-1-002 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

This paper proposes a cooperative mechanism for detecting potentially deceptive cyber attacks that attempt to disregard average voltage regulation and current sharing in cyber-physical dc microgrids. Considering a set of conventional cyber attacks, the detection becomes fairly easy for distributed observer based techniques. However, a well-planned set of balanced attacks, termed as the stealth attack, can bypass the conventional observer based detection theory as the control objectives are met without any physical error involved. In this paper, we discuss the formulation and associated scope of instability from stealth attacks to deceive distributed observers realizing the necessary and sufficient conditions to model such attacks. To address this issue, a novel cooperative vulnerability factor (CVF) framework for each agent is introduced, which accurately identifies the attacked agent(s) under various scenarios. To facilitate detection under worst cases, the CVFs from the secondary voltage control sublayer is strategically cross coupled to the current sublayer, which ultimately disorients the control objectives in the presence of stealth attacks and provides a clear norm for triggering defense mechanisms. Finally, the performance of the proposed detection strategy is simulated in MATLAB/SIMULINK environment and experimentally validated for false data injection and stealth attacks on sensors and communication links.

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) attack is a hot topic in large-scale Cyber-Physical Systems (CPSs), which can cause bad state estimation of controllers. In this paper, we focus on FDI detection on transmission lines of the smart grid. We propose a novel and effective detection framework to identify FDI attacks. Different from the previous methods, there are multi-tier detectors which utilize edge nodes such as the programmable logic controllers (PLCs) instead of the central controller to detect attacks. The proposed framework can decrease the transmission time of data to reduce the latency of decisions because many sensory data need not be transmitted to the central controller for detection. We also develop a detection algorithm which utilizes classifiers based on machine learning to identify FDI. The training process is split from every edge node and is placed on the central node. The detectors are lightweight and are properly adopted in our detection framework. Our simulation experiments show that the proposed detection framework can provide better detection results than the existing detection approaches.

The smart grid is a key piece of infrastructure and its security has attracted widespread attention. The false data injection (FDI) attack is one of the important research issues in the field of smart grid security. Because this kind of attack has a great impact on the safe and stable operation of the smart grid, many effective detection methods have been proposed, such as an FDI detector based on the support vector machine (SVM). In this paper, we first analyze the problem existing in the detector based on SVM. Then, we propose a new attack method to reduce the detection effect of the FDI detector based on SVM and give a proof. The core of the method is that the FDI detector based on SVM cannot detect the attack vectors which are specially constructed and can replace the attack vectors into the training set when it is updated. Therefore, the training set is changed and then the next training result will be affected. With the increase of the number of the attack vectors which are injected into the positive space, the hyperplane moves to the side of the negative space, and the detection effect of the FDI detector based on SVM is reduced. Finally, we analyze the impact of different data injection modes for training results. Simulation experiments show that this attack method can impact the effectiveness of the FDI detector based on SVM.

2017 IEEE Power & Energy Society General Meeting, Cyber-attacks poses a serious threat to power system operation. False data injection attack (FDIA) is one such severe threat, if wisely constructed, can cause flawed estimation of power system states, thereby, leading to uneconomical and unsecured operation of power system. In recent years many methods are proposed to secure the smart grid against malicious cyber-events by protecting certain critical measurement sensors. However, making a system completely hack-proof is rather idealistic. In this paper, in addition to the research carried out in this space, we present a new Log transformation based method to detect the FDIA in real time with high probability. The detection probability of the proposed scheme is compared with existing method using IEEE 14 bus system.

Although the advancement of cyber technologies in sensing, communication and smart measurement devices significantly enhanced power system security and reliability, its dependency on data communications makes it vulnerable to cyber-attacks. Coordinated false data injection (FDI) attacks manipulate power system measurements in a way that emulate the real behaviour of the system and remain unobservable, which misleads the state estimation process, and may result in power outages and even system blackouts. In this paper a robust dynamic state estimation (DSE) algorithm is proposed and implemented on the massively parallel architecture of graphic processing unit (GPU). Numerical simulation on IEEE-118 bus system demonstrate the efficiency and accuracy of the proposed mechanism.

IEEE Transactions on Industrial Informatics, For reliable operation and control of smart grid, estimating the correct states is of utmost importance to the system operator. With recent incorporation of information technology and advanced metering infrastructure, the futuristic grid is more prone to cyber-threats. The false data injection (FDI) attack is one of the most thoroughly researched cyber-attacks. Intelligently crafted, it can cause false estimation of states, which further seriously affects the entire power system operation. In this paper, we propose joint-transformation-based scheme to detect FDI attacks in real time. The proposed method is built on the dynamics of measurement variations. Kullback-Leibler distance is used to find out the difference between probability distributions obtained from measurement variations. The proposed method is tested using IEEE 14 bus system considering attack on different state variables. The results shows that the proposed scheme detects FDI attacks with high detection probability

Conventional power system was originally designed to provide efficient and reliable power. With the integration of information technology and advanced metering infrastructure, the power grid has become smart. The smart meters have allowed the system operators to continuously monitor the power system in real time and take necessary action to avoid system failures. Malicious actor, with access to the smart meters can modify sensor measurements to disrupt the operation of power system. To make the power system resilient to such cyber-attacks, it is important to study all possible outcomes of cyber-intrusions. In this paper, we present an attack on security constrained optimum power flow. We show with the help of case studies how an attacker, by injecting false data in load measurement sensors, can force system operator to change the dispatch and hence make the power system N–1 in-compliant. The attack is modeled as a bi-level optimization problem, aiming to find the minimum set of sensors required to launch the attack. From the system operator's perspective, critical lines and critical generators vulnerable to false data injection (FDI) attack are identified. IEEE 14 bus and 30 bus test systems are used to test the vulnerability of the power system against FDI attacks.

Smart grid cyber-security has come to the forefront of national security priorities due to emergence of new cyber threats such as the False Data Injection (FDI) attack. Using FDI, an attacker can intelligently modify smart grid measurement data to produce wrong system states which can directly affect the safe operation of the physical grid. The goal of this thesis is to investigate key research problems leading to the discovery of significant vulnerabilities and their impact on smart grid operation. The first problem investigates how a stealthy FDI attack can be constructed without the knowledge of system parameters, e.g., line reactance, bus and line connectivity. We show how an attacker can successfully carry out an FDI attack by analysing subspace information of the measurement data without requiring the system topological knowledge. In addition, we make a critical observation that existing subspace based attacks would fail in the presence of gross errors and missing values in the observed data. Next, we show how an attacker can circumvent this problem by using a sparse matrix separation technique. Extensive evaluation on several benchmark systems demonstrates the effectiveness of this approach. The second problem addresses the scenario when an attacker may eavesdrop but only has access to a limited number of measurement devices to inject false data. We show how an attack can be constructed by first estimating the hidden system topology from measurement data only and then use it to identify a set of critical sensors for data injection. Extensive experiments using graph-theoretic and eigenvalue analyses demonstrate that the estimated power grid structure is very close to the original grid topology, and a stealthy FDI attack can be carried out using only a small fraction of all available sensors. The third problem investigates a new type of stealthy Load Redistribution (LR) attack using FDI which can deliberately cause changes in the Locational Marginal Price (LMP) of smart grid nodes. To construct the LR-FDI attack, the Shift factor is estimated from measurement and LMP data. Finally, the impact of the attacks on the state estimation and the nodal energy prices is thoroughly investigated.