In a false data injection attack (FDIA), an adversary stealthily compromises measurements from electricity grid sensors in a coordinated fashion, with a view to evading detection by the power system bad data detection module. A successful FDIA can cause the system operator to perform control actions that compromise either the physical or economic operation of the power system. In this letter, we consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event.

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The 2015 Ukraine Blackout: Implications for False Data Injection Attacks

With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.

A Review of False Data Injection Attacks Against Modern Power Systems

The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations

State estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.

Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks

This paper analyzes a coordinated cyber–physical attack on power systems, which could lead to undetectable line outages. Coordinated with physical attacks that cause line outages, the two-step cyberattacks comprising topology preserving and load redistribution attacks could mask and potentially exasperate outages to trigger cascading failures. These coordinated cyber–physical attacks are analyzed in a proposed bilevel model, which aims at identifying the most damaging and undetectable physical attacks constrained by attackers’ total budget. After being transformed into a mixed-integer linear programming problem, the proposed bilevel model is solved by a rigorous two-stage solution approach. This paper also discusses the relevant countermeasure strategies. The proposed model, the solution algorithm, and the effectiveness of countermeasures are examined by case studies based on the IEEE 14- and 118-bus test systems.

Bilevel Model for Analyzing Coordinated Cyber-Physical Attacks on Power Systems

Modern power grids are becoming more prone to cyberattacks. Even worse, an attacker without the full topology and parameter information of a power grid can still execute a false data injection attack without being detected by the state estimator. This paper proposes an efficient strategy for determining the optimal attacking region that requires reduced network information. The effectiveness of the proposed algorithm is verified through extensive simulations. This paper introduces a new front in the study of smart grid cyber security: determination of a feasible attacking region by obtaining less network information. This paper is also essential and significant for finding effective protection strategies against false data injection attacks based on the deep understanding of the mechanisms and strategies of the attacks.

Modeling of Local False Data Injection Attacks With Reduced Network Information

This paper presents integrating the Battery-based Energy Storage Transportation (BEST) into optimization of generation and transmission planning in electric power system. BEST is the transportation of modular battery-based energy storage (BES) with gird-connection capabilities on train cars via existing railroad tracks to grid locations. BEST not only provides energy storage, but also transport the energy from source to load center. This makes BEST a better way to substitute generation and transmission planning within its ability. The objective function of this problem is minimizing the total system planning cost which consisted with the investment cost of candidate generation units, transmission lines and BES, operation cost of all running generation units and BES, and penalty of loss of load. A 6-bus system with 3 stations which can charging/discharging with grid is used in the numerical simulations, and the results shows the flexibility and optimization of BES compared to generation and transmission planning only. Renewable energy sources (RES) as one kind of candidate units will be added to the problem.

Long-Term Planning with Battery-Based Energy Storage Transportation in Power System

This paper studies how to sample load more realistically and efficiently for security constraint unit commitment (SCUC) problems in order to achieve a high degree of robustness of the unit commitment (UC) solution. For example, given the UC solution, 95% of load profiles can be supplied. Principal component analysis (PCA) is introduced to find a clear feature of the historical load in two-dimensional space rather than the original high-dimensional load space, whose feature is hard to capture. Kernel density estimation (KDE) with Gaussian kernel is applied to form the probability density function (PDF), from which hourly load profiles are sampled. The load profiles based on sparse sampling are used to find the UC solution with high robustness while the load profiles based on dense sampling are used to verify the robustness of the obtained UC solution.

Dan Lu

Papers

Modeling of Local False Data Injection Attacks With Reduced Network Information

Long-Term Planning with Battery-Based Energy Storage Transportation in Power System

Load sampling for scuc based on principal component analysis and kernel density estimation