Bad Data Detection Method Research Articles

Metaheuristics based dimensionality reduction with deep learning driven false data injection attack detection for enhanced network security

Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements. These approaches make certain assumptions regarding false data injection (FDI) attacks, which are inadequate and restrictive to cope with cyberattacks. The reliance on communication technology has emphasized the exposure of power systems to FDI assaults that can bypass the current bad data detection (BDD) mechanism. The current study on unobservable FDI attacks (FDIA) reveals the severe threat of secured system operation because these attacks can avoid the BDD method. Thus, a Data-driven learning-based approach helps detect unobservable FDIAs in distribution systems to mitigate these risks. This study presents a new Hybrid Metaheuristics-based Dimensionality Reduction with Deep Learning for FDIA (HMDR-DLFDIA) Detection technique for Enhanced Network Security. The primary objective of the HMDR-DLFDIA technique is to recognize and classify FDIA attacks in the distribution systems. In the HMDR-DLFDIA technique, the min–max scalar is primarily used for the data normalization process. Besides, a hybrid Harris Hawks optimizer with a sine cosine algorithm (hybrid HHO‐SCA) is applied for feature selection. For FDIA detection, the HMDR-DLFDIA technique utilizes the stacked autoencoder (SAE) method. To improve the detection outcomes of the SAE model, the gazelle optimization algorithm (GOA) is exploited. A complete set of experiments was organized to highlight the supremacy of the HMDR-DLFDIA method. The comprehensive result analysis stated that the HMDR-DLFDIA technique performed better than existing DL models.

Optimal False Data Injection Attacks Against Power System Frequency Stability

The automatic generation control (AGC) is one of the core control systems in power grids that regulate frequency within the permissible range. However, its dependence on communication makes it highly vulnerable to cyber-attacks. An arbitrary false data injection attack (FDIA) on AGC frequency and tie-line flow measurements will likely be detectable by bad data detection methods; however, if an attack can be launched optimally, it often becomes stealthy. In this regard, we develop a framework of optimal FDIAs (OFDIAs) to demonstrate the feasibility of such attacks in the power system frequency control loop. We propose a linearized formulation of discretized power systems’ dynamics in an optimization framework to model OFDIAs that compromise the AGC system by corrupting tie-line flow and generators’ frequency measurements. Using the proposed formal modeling, we study the effects of two types of FDIAs, continuous and time-limited, on the frequency behavior in power grids. The results demonstrate that continuous OFDIAs can lead to severe consequences on a power grid’s performance, such as frequency instability. In contrast, the time-limited FDIAs can cause the frequency to fluctuate beyond the acceptable range, which may lead to the triggering of the frequency-based protection relays.

A Highly Discriminative Detector Against False Data Injection Attacks in AC State Estimation

False data injection attacks (FDIAs) can bypass conventional bad data detection methods. Recently developed FDIA detection methods based on statistical consistency of measurement values may not work effectively when false data do not significantly deviate from historical trends. They may also mistakenly treat actual power grid events as FDIAs. In this paper, a highly discriminative FDIA detector named the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -smallest residual similarity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> SRS) test is proposed. The method is based on the rationale that perfect FDIAs can hardly be achieved in AC state estimation, and real-world imperfect FDIAs always lead to subtle changes in the probability distributions of measurement residuals. Therefore, the statistical consistency of measurement residuals can be carefully portrayed to detect practical FDIAs in AC state estimation. Herein, the Jensen-Shannon distance (JSD) is used to precisely quantify the similarity of measurement residual distributions. Simulations on the IEEE 30-bus system demonstrate that the proposed method can achieve high detection rates and low false alarm rates under a variety of conditions where existing methods do not yield satisfactory results.

Detection of false data injection attack in power information physical system based on SVM–GAB algorithm

Cyber physical power system (CPPS) is highly dependent on information and communication technology, which makes it vulnerable to network attacks. Among them, false data injection attack (FDIA) is not easy to be found by traditional bad data detection methods, and becomes one of the main threats to the safe operation of power systems. However, the high complexity, large amount of data and transient characteristics of CPPS put forward higher requirements for the accuracy and efficiency of FDIA detection method. Therefore, in view of the characteristics of CPPS, this paper proposes SVM–GAB (Support Vector Machines–Gentle​ Adaboost) algorithm to effectively detect FDIA. Through the effective dimension reduction and classification of the measured data, the real-time and high-precision detection of FDIA is realized. This algorithm is compared with mainstream detection algorithms in IEEE-14 and IEEE-39 standard systems. The results show that the false alarm rate of this algorithm is reduced by at least 25% compared with the traditional detection algorithm, and the accuracy and real-time performance of the proposed detection algorithm are verified by experiments.

Formulating false data injection cyberattacks on pumps’ flow rate resulting in cascading failures in smart water systems

As digital interconnections of smart cities greatly enhance the quality of life for residents, they also introduce challenges related to cybersecurity that may adversely impact critical infrastructure. Smart cities must secure their critical infrastructures against cyberattacks. Water distribution is one of these infrastructures. In this paper, a bi-level optimization model is developed for cyberattacks that can result in physical consequence of multiple node failures in water networks. Unlike the existing research that considered random cyberattacks, the proposed cyber–physical attack model injects targeted false data into pumps’ measurements causing pressure head buildup in targeted nodes. In addition, the injected false data will ensure bypassing the existing water system’s state-estimation and bad data detection methods, a condition that was not considered in previously developed attacks. The findings indicate the minimum number of targeted pumps required to create the most severe cascading failures across the water network. The model will identify strategic pumps that are likely to be targeted by cyberattacks, and therefore should be given extra layers of protection by water authorities. • A cybersecurity attack model in water distribution systems is proposed. • Minimum false data is injected on pump’s measurements to increase nodal heads. • Attack model bypasses state estimation causing cascading failures in water systems. • A nonlinear bi-level optimization problem is developed as the cyberattack model.

Sequential false data injection cyberattacks in water distribution systems targeting storage tanks; a bi-level optimization model

While digital interconnectivity of smart cities has significantly enhanced the quality of life of residents, it has introduced cybersecurity challenges that may interrupt the operation of critical infrastructures. Water distribution systems are among the most critical infrastructures in smart cities that need to be secured against potential cyberattacks. False data injection cyberattacks in water distribution systems can be designed to bypass bad data detection algorithms, generating false measurements that ultimately lead to cascading failures. Developing models for these cyberattacks and analyzing them will elucidate hidden layers and tactics used to design the attacks, helping water systems’ authorities to improve and upgrade state-estimation processes and detection algorithms accordingly. In this paper, a bi-level nonlinear optimization cyberattack model is proposed that will result in sequential tank’s overflow or fully withdrawn in less than three hours. The attack model is developed based upon injecting false data into the hourly measurements of the pump(s) feeding the tank, as well as the total demand of the network. By modifying a few estimation parameters in the neighborhood of the targeted tank, these false data injections are deliberately designed to bypass the existing water system’s state-estimation and bad data detection methods.

Идентификация кибер- атак на системы SCADA и СМПР в ЭЭС при обработке измерений методами оценивания состояния

The hardware and software tools of the data acquisition and processing systems, as well as the state estimation procedure intended to support the actions of dispatching personnel in performing operational and emergency control of electric power systems (EPS), are critically important components of the EPS information and communication subsystem, but at the same time, they are most vulnerable to cyberattacks. To reduce the extent to which cyberattacks can affect the control quality, it is proposed to use statistical methods for processing measurement information. First of all, these are static and dynamic state estimation methods, including a procedure for verifying measurements or detecting bad data. An analysis of data quality can determine the type of cyberattack undertaken and identify overlooked vulnerabilities. The article presents the findings from a study of two most commonly used bad data detection methods: the a priori method for analyzing the residuals of test equations and the a posteriori method for analyzing the weighted estimation residuals to identify data distorted as a consequence of specially generated cyberattacks. An algorithm to detect erroneous measurements that appear during cyberattacks and are not identified by conventional measurement verification methods in performing EPS state estimation is proposed

Detecting False Data Injection Attacks Using Canonical Variate Analysis in Power Grid

With the knowledge of the measurement configuration and the topology structure of a power system, attackers can launch false data injection attacks (FDIAs) without detection by existing bad data detection methods in state estimation. The attacks can also introduce errors to estimated state variables, which are critical to grid reliability and operation stability. Existing protection methods cannot handle dynamic and variable network configurations. In this paper, to effectively defend against FDIAs, we propose a canonical variate analysis based detection method which monitors the variation of statistical detection indicators $\boldsymbol{T}^2$ and $\boldsymbol{Q}$ about projected canonical variables before and after attacks. Unlike most statistic models that only consider cross-correlation of discrete measurements constrained by Kirchhoff's Law at each independent sampling time, we also consider the auto-correlation of measurements caused by time series characteristics of varying loads. Experiment results on IEEE-14 bus system demonstrate the effectiveness and accuracy of our proposed method based on both synthetically generated data and real-world electricity data from the New York independent system operator.

Ensemble CorrDet with adaptive statistics for bad data detection

Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing number of power systems, SG data becomes increasingly vulnerable to cyber-attacks. Classic analytic physics-model based bad data detection methods may not detect these attacks. Recently, physics-model and data-driven methods have been proposed to use the temporal aspect of the data to learn multivariate statistics of the SG such as mean and covariance matrices of voltages, power flows etc., and then make decisions based on fixed values of these statistics. However, as loads and generation change within a system, these statistics may change rapidly. In this study, an adaptive data-driven anomaly detection framework, Ensemble CorrDet with Adaptive Statistics (ECD-AS), is proposed to detect false data injection cyber-attacks under a constantly changing system state. ECD-AS is tested on the IEEE 118-bus system for 15 different sets of training and test datasets for a variety of current state-of-the-art bad data detection strategies. Experimental results show that the proposed ECD-AS solution outperforms the related strategies due to its unique ability to capture and account for rapidly changing statistics in SG.

Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer

This article investigates the detection and isolation of false data injection (FDI) attacks in a smart grid based on the unknown input (UI) interval observer. Recent studies have shown that the FDI attacks can bypass the traditional bad data detection methods by using the vulnerability of state estimation. For this reason, the emergency of FDI attacks brings enormous risk to the security of smart grid. To solve this crucial problem, an UI interval observer-based detection and the isolation scheme against FDI attacks are proposed. We first design the UI interval observers to obtain interval state estimation accurately, based on the constructed physical dynamics grid model. Through the capabilities of the designed UI interval observers, the accurate interval estimation state can be decoupled from unknown disturbances. Based on the characteristics of the interval residuals, a UI interval observer-based global detection algorithm was proposed. Particularly, the interval residual-based detection criteria can address the limitation of the precomputed threshold in traditional bad data detection methods. On this basis, we further consider the detection and isolation of FDI attacks under structure vulnerability. Namely, there exist undetectable FDI attacks in the grid system. Taking the attack undetectability problem into account, a logic judgment matrix-based local detection and isolation algorithm against FDI attacks are developed. Based on the combinations of observable sensor cases, local control centers can further detect and isolate the attack set under structure vulnerability. Finally, the effectiveness of the developed detection and isolation algorithms against FDI attacks is demonstrated on the IEEE 8-bus and IEEE 118-bus smart grid system, respectively.

Data-Quality Aware Distribution State Estimation Using Maximum Normal Measurement Rate

State estimation application in the distribution system is being revisited with the availability of additional sensor data enabled by the distribution automation and massive sensors installation in the electric power grid. Some of these sensor data have measurement error, data quality problems, and different reporting rate. The traditional weighted least square estimation (WLSE) method is used to find best fit between the system model and the measured data. WLSE will typically give low performance and poor accuracy when some measurements have excessively large error compared to the rest of the measurements. This work proposes a maximum normal measurement rate (MNMR) based state estimation with simultaneous bad data detection method and considering three-phase unbalanced systems. The simulation results for an IEEE test system and an industrial feeder demonstrate the MNMR-based state estimation provides better performance even in the presence of multiple bad measurement data.

Bad Data Detection Algorithm for PMU Based on Spectral Clustering

Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.

Detection and Isolation of False Data Injection Attacks in Smart Grids via Nonlinear Interval Observer

This article investigates the detection and isolation of false data injection (FDI) attacks in a smart grid based on the unknown input (UI) interval observer. Recent studies have shown that the FDI attacks can bypass the traditional bad data detection methods by using the vulnerability of state estimation. For this reason, the emergency of FDI attacks brings enormous risk to the security of smart grid. To solve this crucial problem, an UI interval observer-based detection and the isolation scheme against FDI attacks are proposed. We first design the UI interval observers to obtain interval state estimation accurately, based on the constructed physical dynamics grid model. Through the capabilities of the designed UI interval observers, the accurate interval estimation state can be decoupled from unknown disturbances. Based on the characteristics of the interval residuals, a UI interval observer-based global detection algorithm was proposed. Particularly, the interval residual-based detection criteria can address the limitation of the precomputed threshold in traditional bad data detection methods. On this basis, we further consider the detection and isolation of FDI attacks under structure vulnerability. Namely, there exist undetectable FDI attacks in the grid system. Taking the attack undetectability problem into account, a logic judgment matrix-based local detection and isolation algorithm against FDI attacks are developed. Based on the combinations of observable sensor cases, local control centers can further detect and isolate the attack set under structure vulnerability. Finally, the effectiveness of the developed detection and isolation algorithms against FDI attacks is demonstrated on the IEEE 8-bus and IEEE 118-bus smart grid system, respectively.

Review of the false data injection attack against the cyber‐physical power system

With the development of synchronous measuring technology and communication technology, the units of measurement, calculation, execution and communication are deeply integrated into energy manage system, which can achieve panoramic state awareness through the fast and accurate state estimation algorithm. Meanwhile, the cyber-attack has become an important issue posing severe threats to the secure operation of power systems. A well-designed false data injection attack (FDIA) against state estimation can effectively bypass the traditional bad data detection methods and interfere with the decision of the control centre, thus causing the power system incidents. This study comprehensively discusses the characteristics of FDIA including not only the goals, construction methods and consequences of FDIA from the perspective of attackers but also the protection and detection countermeasures from the perspective of defenders. Moreover, a game-theory-based FDIA against the substation information network is simulated to reveal the interactions between attackers and defenders.

Reactance Perturbation for Detecting and Identifying FDI Attacks in Power System State Estimation

False data injection (FDI) attacks have recently been introduced as an important class of cyberattacks in modern power systems. By coordinating the injection of false data in selected meters readings, an FDI attacks can bypass bad data detection methods in power system state estimation. In this paper, we propose a strategy to enhance detection and identification of an FDI that leverages reactance perturbation. We begin by deriving conditions to mitigate attacks in noiseless systems that relates the likelihood of attack detection and identification to the rank of the composite matrix, limited by power system topology and the deployment of meters. Based on such conditions, we design a secure reactance perturbation algorithm that maximizes the likelihood of an FDI attack detection and identification while minimizing the effect on the operational cost of power systems, e.g., power losses on transmission lines. Simulations on a 6-bus and the IEEE 57-bus system verify the performance of the secure reactance perturbation and the effect on power losses in both noiseless and noisy systems.

Study of Monitoring False Data Injection Attacks Based on Machine-learning in Electric Systems

False data injected by hackers can interfere with power system state estimation and pose a great threat to the safe and reliable operation of modern power systems (FDIA). The traditional bad data detection method can not effectively detect such attacks. In this paper, by extracting relevant power system measurement characteristic value and use the historical data as the sample, using three classical machine learning algorithms (Perceptron, KNN, SVM) of false data injection attack detection, and respectively in IEEE-9, IEEE-57, IEEE-118 simulation platform for test, verify the supervised machine learning algorithm is applied to the validity of false data injection attack detection.

Based on the Fractal Technology of Bad Data Detection Method in Power System

detection and identification of bad data is an important part of state estimation in power system. To solute the problem generates a variety of detection methods and means in academic and industrial circles, commonly used methods include objective function detection, weighted residual detection, measurement suddenly-change detection and the comprehensive application of above methods. In order to detection the bad data from large amounts of data over the multiple sliding windows, bad data detection algorithm is proposed based on fractal technology building monotonic search space. Firstly, it gives the data set on the piecewise fractal model, and then based on this model to design a detection algorithm. The algorithm can reduce detection processing time greatly. The subsection fractal model can accurately model on the data self similarity and compress data. Theoretical analysis and experimental results show that, the algorithm has higher precision and lower time / space complexity, more suitable for bad data detection.

State Estimation for a Power System with Dense and Short Transmission Lines

This paper presents experimental results of applying several state estimation methods to a power system. This power system is located in a small island and has high load density. The fast decoupled weighted least squares method turned out to be not suitable to this system, because the assumption of the decoupling between P and Q is not appropriate, and the magnitude of impedances vary widely. As for the variety of the impedance magnitude, the ratio of the maximum to minimum values is larger than 1000. The usual bad data detection method using the statistical approach also turned out to be not applicable to this system, because of the trouble in maintaining accuracy in numerical calculation. To cope with these troubles, we adopted the weighted least squares method without decoupling between P and Q, and developed a new bad data detection method without matrix computation. The method was validated by numerical experiments.