Search

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

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.

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.

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

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

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.