Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches $l$ in the power system is not less than twice that of the system states $n$ (i.e., $l \geq 2n$, where $n + 1$ is the number of buses); (ii) the susceptances of more than $n$ branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the \emph{stealthy attack space}, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the \emph{stealthy attack space} and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems

Proactive defense strategies against net load redistribution attacks in cyber-physical smart grids

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringHongyu WuRecent advances in the cyber-physical smart grid (CPSG) have enabled a broad range of new devices based on information and communication technology (ICT). An open network environment in CPSG provides frequent interaction between information and physical components. However, this interaction also exposes the ICT-enabled devices to a growing threat of cyberattacks. Such threats have been alerted by recent cybersecurity incidents, and the security issues have strongly restricted the development of CPSG. Among various CPS cybersecurity incidents, cyber data attacks invade the cyber layer to destroy data integrity. Through elaborately eavesdropping on the transferred measurement data, the attacks can mislead the state estimation (SE) while keeping stealthy to conventional bad data detection (BDD). Due to the SE being the critical function of CPSG control, the cyber data attacks may cause massive economic loss, power system instability, or even cascading failures. Therefore, this dissertation focuses on the detection of stealthy data integrity attacks. This dissertation first 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 work 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. In addition, this work discusses several operational and informational defense approaches that present the current state-of-the-art in the field, including moving target defense (MTD), watermarking, and data-driven strategies. The challenges and future opportunities associated with the smart grid cyber-physical security is also discussed. Further, a real-time digital simulator, namely Typhoon HIL, is utilized to visualize the random MTD against false data injection (FDI) attacks. Given the review section as a background, a hidden, coordinated net load redistribution attack (NLRA) in an AC distribution system is proposed. The attacker's goal is to create violations in nodal voltage magnitude estimation. An attacker can implement the NLRA strategy by using the local information of an attack region and power flow enhanced deep learning (PFEDL) state estimators. The NLRA is modeled as an attacker's modified AC optimal power flow problem to maximize the attack impact. Case study results indicate the PFEDL-based SE can provide the attacker with accurate system states in a low observable distribution system where conventional lease square-based SE cannot converge. The stealthiness of the hidden NLRA is validated in multiple attack cases. The influence of NLRA on the distribution system is assessed, and the impact of attack regions, attack timing, and attack area size are also revealed. Next, this dissertation highlights that current MTD strategies myopically perturb the reactance of D-FACTS lines without considering the system voltage stability. Voltage instability induced by MTDs is illustrated in a three-bus system and two more complicated systems with real-world load profiles. Further, a novel MTD framework that explicitly considers system voltage stability using continuation power flow and voltage stability indices is proposed to avoid MTD-induced voltage instability. In addition, this dissertation mathematically derives the sensitivity matrix of voltage stability index to line impedance, on which an optimization problem for maximizing voltage stability index is formulated. This framework is tested on the IEEE 14-bus and the IEEE 118-bus transmission systems, in which sophisticated attackers launch NLRAs. The simulation results show the effectiveness of the proposed framework in circumventing voltage instability while maintaining the detection effectiveness of MTD. Case studies are conducted with and without the proposed framework under different MTD planning and operational methods. The impacts of the proposed two methods on attack detection effectiveness and system economic metrics are also revealed. Finally, this dissertation proposes utilizing smart inverters to implement a novel meter encoding scheme in distribution systems. The proposed meter encoding scheme is a software-based active detection method, which neither requires additional hardware devices nor causes system instability, compared with MTD and watermarking. By elaborately constructing the encoding vector, the proposed smart-inverter-based meter encoding can mislead the attacker's SE while being hidden from alert attackers. In addition, by utilizing the topology of radial distribution systems, the proposed encoding scheme encodes fewer meters than current schemes when protecting the same number of buses, which decreases the encoding cost. Simulation results from the IEEE 69-bus distribution system demonstrate that the proposed meter encoding scheme can mislead the attacker's state estimation on all the downstream buses of an encoded bus without arousing the attacker's suspicion. FDI attacks constructed based on the misled estimated states are highly possible to trigger the defender's BDD alarm

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Smart grid (SG) networks are newly upgraded networks of connected objects that greatly improve reliability, efficiency and sustainability of the traditional energy infrastructure. In this respect, the smart metering infrastructure (SMI) plays an important role in controlling, monitoring and managing multiple domains in the SG. Despite the salient features of SMI, security and privacy issues have been under debate because of the large number of heterogeneous devices that are anticipated to be coordinated through public communication networks. This survey paper shows a brief overview of real cyber attack incidents in traditional energy networks and those targeting the smart metering network. Specifically, we present a threat taxonomy considering: (i) threats in system-level security, (ii) threats and/or theft of services, and (iii) threats to privacy. Based on the presented threats, we derive a set of security and privacy requirements for SG metering networks. Furthermore, we discuss various schemes that have been proposed to address these threats, considering the pros and cons of each. Finally, we investigate the open research issues to shed new light on future research directions in smart grid metering networks

Co-design of Security Aware Power System Distribution Architecture as Cyber Physical System

The modern smart grid would involve deep integration between measurement nodes, communication systems, artificial intelligence, power electronics and distributed resources. On one hand, this type of integration can dramatically improve the grid performance and efficiency, but on the other, it can also introduce new types of vulnerabilities to the grid. To obtain the best performance, while minimizing the risk of vulnerabilities, the physical power system must be designed as a security aware system. In this dissertation, an interoperability and communication framework for microgrid control and Cyber Physical system enhancements is designed and implemented taking into account cyber and physical security aspects. The proposed data-centric interoperability layer provides a common data bus and a resilient control network for seamless integration of distributed energy resources. In addition, a synchronized measurement network and advanced metering infrastructure were developed to provide real-time monitoring for active distribution networks. A hybrid hardware/software testbed environment was developed to represent the smart grid as a cyber-physical system through hardware and software in the loop simulation methods. In addition it provides a flexible interface for remote integration and experimentation of attack scenarios. The work in this dissertation utilizes communication technologies to enhance the performance of the DC microgrids and distribution networks by extending the application of the GPS synchronization to the DC Networks. GPS synchronization allows the operation of distributed DC-DC converters as an interleaved converters system. Along with the GPS synchronization, carrier extraction synchronization technique was developed to improve the system’s security and reliability in the case of GPS signal spoofing or jamming. To improve the integration of the microgrid with the utility system, new synchronization and islanding detection algorithms were developed. The developed algorithms overcome the problem of SCADA and PMU based islanding detection methods such as communication failure and frequency stability. In addition, a real-time energy management system with online optimization was developed to manage the energy resources within the microgrid. The security and privacy were also addressed in both the cyber and physical levels. For the physical design, two techniques were developed to address the physical privacy issues by changing the current and electromagnetic signature. For the cyber level, a security mechanism for IEC 61850 GOOSE messages was developed to address the security shortcomings in the standard

Security and Trust in Safety Critical Infrastructures

Critical infrastructures such as road vehicles and railways are undergoing a major change, which increases the dependency of their operation and control on Information Technology (IT) and makes them more vulnerable to malicious intent. New complex communication infrastructures emerge using the increased connectivity of these safety-critical systems to enable efficient management of operational processes, service provisioning, and information exchange for various (third-party) actors. Railway Command and Control Systems (CCSs) turn with the introduction of digital interlocking into an “Internet of Railway Things”, where safety-critical railway signaling components are deployed on common-purpose platforms and connected via standard IP-based networks. Similarly, the mass adoption of Electric Vehicles (EVs) and the need to supply their batteries with energy for charging has given rise to a Vehicle-to-Grid (V2G) infrastructure, which connects vehicles to power grids and multiple service providers to coordinate charging and discharging processes and maintain grid stability under varying power demands. The Plug-and-Charge feature brought in by the V2G communication standard ISO 15118 allows an EV to access charging and value-added services, negotiate charging schedules, and support the grid as a distributed energy resource in a largely automated way, by leveraging identity credentials installed in the vehicle for authentication and payment. The fast deployment of this advanced functionality is driven by economical and political decisions including the EU Green Deal for climate neutrality. Due to the complex requirements and long standardization and development cycles, the standards and regulations, which play the key role in operating and protecting critical infrastructures, are under pressure to enable the timely and cost-effective adoption. In this thesis, we investigate security and safety of future V2G and railway command and control systems with respect to secure communication, platform assurance as well as safety and security co-engineering. One of the major goals in this context is the continuous collaboration and establishment of the proposed security solutions in upcoming domain-specific standards, thus ensuring their practical applicability and prompt implementation in real-world products. We first analyze the security of V2G communication protocols and requirements for secure service provisioning via charging connections. We propose a new Plug-and-Patch protocol that enables secure update of EVs as a value-added service integrated into the V2G charging loop. Since EVs can also participate in energy trading by storing and feeding previously stored energy to grid, home, or other vehicles, we then investigate fraud detection methods that can be employed to identify manipulations and misbehaving users. In order to provide a strong security foundation for V2G communications, we propose and analyze three security architectures employing a hardware trust anchor to enable trust establishment in V2G communications. We integrate these architectures into standard V2G protocols for load management, e-mobility services and value-added services in the V2G infrastructure, and evaluate the associated performance and security trade-offs. The final aspect of this work is safety and security co-engineering, i.e., integration of safety and security processes vital for the adequate protection of connected safety-critical systems. We consider two application scenarios, Electric Vehicle Charging System (EVCS) and Object Controller (OC) in railway CCS, and investigate how security methods like trusted computing can be applied to provide both required safety and security properties. In the case of EVCS, we bind the trust boundary for safety functionality (certified configuration) to the trust boundary in the security domain and design a new security architecture that enforces safety properties via security assertions. For the railway use case, we focus on ensuring non-interference (separation) between these two domains and develop a security architecture that allows secure co-existence of applications with different criticality on the same hardware platform. The proposed solutions have been presented to the committee ISO/TC 22/SC 31/JWG 1 that develops the ISO 15118 standard series and to the DKE working group “Informationssicherheit für Elektromobilität” responsible for the respective application guidelines. Our security extension has been integrated in the newest edition ISO 15118-20 released in April 2022. Several manufacturers have already started concept validation for their future products using our results. In this way, the presented analyses and techniques are fundamental contributions in improving the state of security for e-mobility and railway applications, and the overall resilience of safety-critical infrastructures to malicious attacks

Towards Scalable, Private and Practical Deep Learning

Deep Learning (DL) models have drastically improved the performance of Artificial Intelligence (AI) tasks such as image recognition, word prediction, translation, among many others, on which traditional Machine Learning (ML) models fall short. However, DL models are costly to design, train, and deploy due to their computing and memory demands. Designing DL models usually requires extensive expertise and significant manual tuning efforts. Even with the latest accelerators such as Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU), training DL models can take prohibitively long time, therefore training large DL models in a distributed manner is a norm. Massive amount of data is made available thanks to the prevalence of mobile and internet-of-things (IoT) devices. However, regulations such as HIPAA and GDPR limit the access and transmission of personal data to protect security and privacy. Therefore, enabling DL model training in a decentralized but private fashion is urgent and critical. Deploying trained DL models in a real world environment usually requires meeting Quality of Service (QoS) standards, which makes adaptability of DL models an important yet challenging matter.  In this dissertation, we aim to address the above challenges to make a step towards scalable, private, and practical deep learning. To simplify DL model design, we propose Efficient Progressive Neural-Architecture Search (EPNAS) and FedCust to automatically design model architectures and tune hyperparameters, respectively. To provide efficient and robust distributed training while preserving privacy, we design LEASGD, TiFL, and HDFL. We further conduct a study on the security aspect of distributed learning by focusing on how data heterogeneity affects backdoor attacks and how to mitigate such threats. Finally, we use super resolution (SR) as an example application to explore model adaptability for cross platform deployment and dynamic runtime environment. Specifically, we propose DySR and AdaSR frameworks which enable SR models to meet QoS by dynamically adapting to available resources instantly and seamlessly without excessive memory overheads

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Laboratory Directed Research and Development Annual Report - Fiscal Year 2000

The projects described in this report represent the Laboratory's investment in its future and are vital to maintaining the ability to develop creative solutions for the scientific and technical challenges faced by DOE and the nation. In accordance with DOE guidelines, the report provides, a) a director's statement, b) an overview of the laboratory's LDRD program, including PNNL's management process and a self-assessment of the program, c) a five-year project funding table, and d) project summaries for each LDRD project

Older than language : comics as philosophical praxis and heuristic for philosophical canon

Includes bibliographical references (leaves 194-197).The central task of this dissertation is the exploration of the medium of comics, and its connections to both popular culture and philosophy as a practice conceived in the Western tradition. Comics (at times referred to as both 'graphic literature' and 'sequential art' during this dissertation) constitutes a wholly new object. One that is qualitatively distinct from prose, theater, poetry and cinema. Mimicking the structure of comics wherein two images are juxtaposed to suggest (rather than explicitly state) a coherent sequence in the mind of the reader, this dissertation offers two "images" of its central thesis: one a theoretical element, the other a work of creative fiction. Following on from each other, these "images" interrogate both in their parts and in their sequence, the politics of representation around comics and its connections to philosophy and the popular. In the first "image" a theoretical work is forwarded to examine the various connections that arise between comics, popular culture and philosophy. The central thesis of this element argues for a nuanced understanding in which the medium of comics provides for a clearer interlocutor of Western philosophy's perennial concerns. The works of Galileo, Vico, Descartes, Darwin, Marx, Freud, Einstein, Foucault and Deleuze are reinterpreted using the aesthetic mechanics of comics as philosophical concept. This dissertation thus asserts that comics functions as "heuristic" for Western philosophy, a method which encodes understanding through practice

Sensing Collectives: Aesthetic and Political Practices Intertwined

Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders