Efficient detection of faults and false data injection attacks in smart grid using a reconfigurable Kalman filter (original) (raw)

Cyberattack Detection in Intelligent Grids Using Non-linear Filtering

2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

Electric power grids are evolving towards intellectualization such as Smart Grids or active-adaptive networks. Intelligent power network implies usage of sensors, smart meters, electronic devices and sophisticated communication network. This leads to a strong dependence on information and communication networking that are prone to threats of cyberattacks, which challenges power system reliability and efficiency. Thus, significant attention should be paid to the Smart Grids security. Recently, it has been proven that False Data Injection Attacks (FDIA) could corrupt results of State Estimation (SE) without noticing, therefore, leading to a possible mis-operation of the whole power system. In this paper, we introduce an algorithm for detecting cyberattacks based on non-linear filtering by using cyber-physical information from Kirchhoff laws. The proposed algorithm only needs data from adjacent nodes, therefore can be locally and distributed implemented. Also, it requires very low computational effort so that it can be run online, and it is suitable for implementation in existing or new ad-hoc low-cost devices. The proposed algorithm could be helpful to increase power system awareness against FDIA complementing the current SE implementations. The efficiency of the proposed algorithm has been proved by mathematical simulations and computer modeling in PSCAD software. Our results show that the proposed methodology can detect cyberattacks to the SE in 99.9% of the cases with very little false alarms on the identification of spoiled measurements (4.6%).

An Integrated Defense Architecture Against False Data Injection Attacks in Smart Grid

—Smart Grid is a new type of power grid that will provide reliable, secure, and efficient energy transmission and distribution in real time. While most existing techniques for protecting power grids were designed to ensure system reliability (e.g., against random failures), recently there is growing concern in smart grid initiatives on the protection against malicious cyber attacks. In particular, the adversary can inject false measurement reports to disrupt the smart grid operation through the compromised meters and sensors. Hence, defending against those false data injection attack becomes a critical issue. Nevertheless, there is no existing solution that considers all aspects such as deployment cost, efficiency, and effectiveness. In this paper, we develop a defense system that integrates the anomaly-based intrusion detection and watermarking-based detection. Our anomaly-based detection can detect strong and rapid attacks. To deal with slow and stealth attacks, we adopt the watermarking-based detection. In particular, we add secure watermarks to real-time meter readings and transmit the watermarked data stream to the utility. The utility can then correlate the watermarked data with the original watermarks (transmitted via a secured channel) to detect the presence of false data injected by the adversary during the data transmission path. Our experimental results show that our integrated defense strategy can accurately detect both strong and stealthy attacks.

A Novel Dynamic Watermarking-Based EKF Detection Method for FDIAs in Smart Grid

IEEE/CAA Journal of Automatica Sinica, 2022

The existing bad data detection (BDD) cannot effectively detect false data injection attacks (FDIAs) in smart grid. The objectiveness of this letter is to investigate a novel dynamic watermarking (DW)-based extended Kalman filter (EKF) detection method to detect FDIAs. Firstly, security weakness of traditional χ2 detector is analyzed, and a novel DW-based EKF detection method is proposed for FDIAs. Secondly, the detection effectiveness and security property of the proposed method are analyzed theoretically, where not only the positive correlation between the detection performance and DW signal intensity but also zero impact of FDIAs not being detected on smart grid (SG) are revealed. Finally, the effectiveness of the proposed method is confirmed by experimental results.

Rule Based Novel Method for Self Healing Attack Revelation for Smart Grids

International Journal of Engineering and Advanced Technology, 2020

In this paper, we introduce a new idea for the rebuilding of measuring sensor data collected from the power grid, eliminating the impact of the attack on the integrity of confidential data. The introduced system is based on the reconstruction of Monte Carlo analysis of experimental data and the measurement of actual training data of the transfer function of the information gathered by sensor of the strong nonlinear representation data through the root is added to the sensor measurements based on quality parameters by a clever attacker. For strong, multivariate reconstruction measures against multiple attacks sensors based regulation attack detection is used. The introduced scheme is check out using a standard IEEE 34-bus and real samples were collected from a grid system. The simulation results confirm that the introduced scheme can handle the label and non-label and attacks based on the proposed rules historical measurement data decided on the basis of the received value RAE become...

False data injection attacks against state estimation in electric power grids

ACM Transactions on Information and System Security, 2011

In this paper we study the effect of false data injection attacks on state estimation carried over a sensor network monitoring a discrete-time linear time-invariant Gaussian system. The steady state Kalman filter is used to perform state estimation while a failure detector is employed to detect anomalies in the system. An attacker wishes to compromise the integrity of the state estimator by hijacking a subset of sensors and sending altered readings. In order to inject fake sensor measurements without being detected the attacker will need to carefully design his actions to fool the estimator as abnormal sensor measurements would result in an alarm. It is important for a designer to determine the set of all the estimation biases that an attacker can inject into the system without being detected, providing a quantitative measure of the resilience of the system to such attacks. To this end, we will provide an ellipsoidal algorithm to compute its inner and outer approximations of such set. A numerical example is presented to further illustrate the effect of false data injection attack on state estimation.

Detection of false data injection attacks in smart-grid systems

IEEE Communications Magazine, 2015

Smart grids are essentially electrical grids that uses information and communication technology (ICT) to provide reliable, efficient electricity transmission and distribution. Security and trust are of paramount importance. Among various emerging security issues, false data injection (FDI) attack is one of the most substantial ones, which can significantly increase the cost of the energy distribution process. However, most current research focuses on countermeasures to FDIs for traditional power grids rather smart grid infrastructures. We develop an efficient and real-time scheme to detect FDI attacks in smart grids, by exploiting spatial-temporal correlations between grid components. Through realistic simulations based on the US smart grid, we demonstrate that the proposed scheme provides an accurate and reliable solution.

Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis

IEEE IECON, 2019

False Data Injection (FDI) is one of the most dangerous attacks on cyber-physical systems as it could lead to disastrous consequences in the operation of the power grids. In this paper, a comprehensive investigation of the (FDI) attacks in smart grids is presented. A detection algorithm is utilized in analyzing the FDI attacks in real-time environment based on Principle Component Analysis (PCA). It provides an adequate solution to the FDI problem for its ability to extract information about correlation of the collected measurements. This provides a more accurate and sensitive response than the previous FDI detection techniques. Furthermore, the light computations associated with this algorithm make it a very good candidate for real-time environment testing. The results concluded in the paper illustrate a very promising future for the PCA-based realtime FDI attack detection schemes.

Vulnerabilities of Smart Grid State Estimation against False Data Injection Attack

In recent years, Information Security has become a notable issue in the energy sector. After the invention of ‘The Stuxnet worm’ [1] in 2010, data integrity, privacy and confidentiality has received significant importance in the real-time operation of the control centres. New methods and frameworks are being developed to protect the National Critical Infrastructures like- energy sector. In the recent literatures, it has been shown that the key real-time operational tools (e.g., State Estimator) of any Energy Management System (EMS) are vulnerable to Cyber Attacks. In this chapter, one such cyber attack named ‘False Data Injection Attack’ is discussed. A literature review with a case study is considered to explain the characteristics and significance of such data integrity attacks.

Subset Level Detection of False Data Injection Attacks in Smart Grids

State estimation is a critical component in determining whether the power grid is operating properly, or not. Invalid state estimate can have a huge impact on the stability of the grid and can cause severe socioeconomic damage. False data injection attacks (FDIAs) display a prominent threat to the operation of power systems, especially when carefully constructed to bypass traditional bad data detection (BDD). Therefore, an intrusion detection system (IDS) has to be in place to prevent FDIAs from going unnoticed. A major limitation of current approaches is that only coarse-grained attack detection is performed. In order to take effective mitigation actions, it would be more beneficial to detect whether any critical subset of state variables is under attack or not. In this paper, we investigate two state-of-the-art machine learning algorithms for subset level detection of FDIAs. Furthermore, the trade-off between performance and subset size is investigated. The proposed detection algorithms are evaluated by simulating FDIAs on the IEEE 30-bus system using real-world load data for measurement construction. Index Terms-Subset level detection, state estimation, false data injection attack, machine learning, support vector machine, recurrent neural network, long short-term memory unit.

Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

IEEE Transactions on Industrial Informatics, 2015

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems. Index Terms-Bad data detection (BDD), malicious data attack, robust principle component analysis (PCA), smart grid security. I. INTRODUCTION C OMPARED with the traditional power grids, a smart grid tends to be much more reliable, efficient, and intelligent due to the remarkable advancements in sensing, monitoring, control technologies, and also the tight integration with cyber infrastructure and advanced computing and communication technologies [1]. However, this integration can lead to new vulnerabilities to cyber attacks on the power systems. Cyber attacks are reported as one of the main potential threats to the reliable operation of the power system [2], [3]. In this paper, we Manuscript