False data injection attacks detection on power systems with convolutional neural network

The smart grid combines the classical power system with the information technology, leading to a cyber-physical system. In such an environment, the malicious injection of data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect well designed false data injection (FDI) attacks. Moreover, most of the works on FDI attack detection are based on the linearized DC model of the power system and fails to detect attacks based on the AC model. The aim of this paper is to address these problems by using the graph structure of the grid and the AC power flow model. We derive an attack detection method that is able to detect previously undetectable FDI attacks. This method is based on concepts originating from graph signal processing (GSP). The proposed detection scheme calculates the graph Fourier transform of an estimated grid state and filters the graph’s high-frequency components. By comparing the maximum norm of this outcome with a threshold, we can detect the presence of FDI attacks. Case studies on the IEEE 14-bus system demonstrate that the proposed method is able to detect a wide range of previously undetectable attacks, both on angles and on magnitudes of the voltages.

To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.

The work presented in this chapter is an extension of our previous research of bringing together the Cognitive Dynamic System (CDS) and the Smart Grid (SG) by focusing on AC state estimation and Cyber-Attack detection. Under the AC power flow model, state estimation is complex and computationally expensive as it relies on iterative procedures. On the other hand, the False Data Injection (FDI) attacks are a new category of cyber-attacks targeting the SG that can bypass the current bad data detection techniques in the SG. Due to the complexity of the nonlinear system involved, the amount of published works on AC based FDI attacks have been fewer compared to their DC counterpart. Here, we will demonstrate how the entropic state, which is the objective function of the CDS, can be used as a metric to monitor the grid’s health and detect FDI attacks. The CDS, acting as the supervisor of the system, improves the entropic state on a cycle to cycle basis by dynamically optimizing the state estimation process through the reconfiguration of the weights of the sensors in the network. In order to showcase performance of this new structure, computer simulations are carried out on the IEEE 14-bus system for optimal state estimation and FDI attack detection.

Advanced false data injection attack in targeted malware intrusion is becoming an emerging severe threat to the Supervisory Control And Data Acquisition (SCADA) system. Several intrusion detection schemes have been proposed previously [1, 2]. However, designing an effective real-time detection system for a resource-constraint device is still an open problem for the research community. In this paper, we propose a new relation-graph-based detection scheme to defeat false data injection attacks at the SCADA system, even when injected data may seemly fall within a valid/normal range. To balance effectiveness and efficiency, we design a novel detection model, alternation vectors with state relation graph. Furthermore, we propose a new inference algorithm to infer the injection point(s), i.e., the attack origin, in the system. We evaluate SRID with a real-world power plant simulator. The experiment results show that SRID can detect various false data injection attacks with a low false positive rate at 0.0125%. Meanwhile, SRID can dramatically reduce the search space of attack origins and accurately locate most of attack origins.KeywordsIntrusion Detection SystemCyber Security in SCADAFalse Data Injection Attack

In this study, to enhance the system reliability under false data injection (FDI) attacks and DC-link voltage (DCLV) sensor failures, a hybrid control strategy for a DC microgrid (DCMG) based on the Lagrange extrapolation and voltage observer is proposed. Under normal conditions without FDI attacks or DCLV sensor failures, the DCMG system works in a distributed control scheme. To enhance the reliability of the system under the DCLV sensor failure or FDI attack, the DCMG system utilizes a hybrid control strategy that combines distributed control with decentralized control. The hybrid control strategy is achieved by the proposed detection algorithms for FDI attacks and DCLV sensor failures. The detection of FDI attacks is accomplished by comparing the predicted secondary controller output based on the Lagrange extrapolation with the actual one. When a power agent detects an FDI attack, its control mode is switched to decentralized control by using the proposed hybrid control strategy. The DCLV sensor failure detection algorithm to enhance system reliability against DCLV sensor failures is achieved by comparing the estimated DCLV with the measured one from the voltage observer. Upon detecting a DCLV sensor failure, the operation of the power agent is switched to the current control mode to sustain the system operation even under DCLV sensor failures. The proposed detection algorithms are simple, effective, and precise, operating without mutual interference that deteriorates the detection accuracy. Simulation and experiments are carried out under various uncertain test conditions to validate the reliability and effectiveness of the proposed control strategy.

A quickest intrusion detection algorithm is proposed to detect false data injection attacks (FDIAs) in smart grids with time-varying dynamic models. The quickest detection algorithm aims at minimizing the worst case detection delays (WDDs) of cyberattacks, subject to an upper bound of the false alarm rate. Since power-grid state transitions could be caused by either cyberattacks or sudden change in loads or grid configurations, we propose to distinguish between an FDIA and a sudden system change by using a time-varying dynamic model, which can accurately capture the dynamic state transitions due to changes in system configurations. A dynamic state estimation algorithm is developed to estimate and track the time-varying and nonstationary power-grid states. The quickest detection algorithm is developed by analyzing the statistical properties of dynamic state estimations, such that the algorithm minimizes the WDD while accurately distinguishing FDIA from sudden system changes. A Markov-chain-based analytical model is used to identify the detector’s parameter and quantify its performance. Simulation results demonstrate that the proposed algorithm can accurately detect and remove false data injections or system faults with minimum delays. The proposed algorithm can be implemented to harden intelligent electronic devices (IEDs) or supervisory control and data acquisition (SCADA) systems to improve their resilience to cyberattacks or system faults, thus improving the cybersecurity of smart grids.

This paper introduces Moving Target Defense (MTD) in distribution system against False Data Injection (FDI) attacks on the supervisory control and data acquisition (SCADA) system. Based on the AC power flow model, a hidden MTD (HMTD) strategy is constructed in combination with network reconfiguration by minimizing the system loss and line power flow differences before and after the HMTD. The proposed HMTD-based network reconfiguration is formulated as a mixed-integer nonlinear programming (MINLP) problem. A refined Genetic Algorithm (GA) is proposed to solve it. Numerical test is conducted in a modified IEEE-66 bus system. The simulation results show that the proposed model reduces the power loss introduced by the HMTD as well as yields a stealthy MTD to the attackers. The impact of HMTD on the system performance is also compared with that of the existing MTD strategies.

The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids.

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The rapid adoption of the Industrial Internet of Things (IIoT) model has exposed methods to inadequate security measures. False data injection attacks (FDIAs) pose a significant security threat in IIoT, as they aim to mislead industrial platforms by manipulating sensor readings. In IIoT, owing to the data attacker’s changeability, the FDIA is the most critical intrusion network. As aggressive devices permitted in the network perform their typical data-collecting tasks, identifying an FDIA becomes a nontrivial effort due to potential attacks. Conventional attack detection models have proven insufficient in addressing FDIAs, and most current countermeasures focus on the need to legalise data, primarily in the context of data clustering services. Presently, deep learning (DL) systems are employed for detecting FDIA in real-time and supply personalised protective measures to the threat. This paper presents an Intelligent Management of False Data Injection Attacks Using Feature Selection and Voting Classifier (IMFDIA-FSVC) technique in IIoT systems. The primary purpose of the IMFDIA-FSVC technique is to develop a model for detecting and mitigating FDIAs to ensure safe and trustworthy operations of IIoT systems. Initially, the data pre-processing stage involves two stages: missing value analysis and normalisation to standardise the input data for an effective study. For an effective feature selection, the IMFDIA-FSVC model utilises a statistical and information-theoretic selection (SITS) technique to select optimal features from the input data. Finally, the classification process is mainly deployed by three models: the temporal convolutional network (TCN), the deep belief network (DBN), and the autoencoder (AE). An ensemble classifier is then performed using the voting classifier. The comparison study of the IMFDIA-FSVC method showed a superior accuracy value of 99.15% compared to existing models on the IIoT and FDIA datasets.

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

This paper mainly studies vector-related learning and pairwise optimization feature selection for false data injection attack detection in smart grid. In order to provide experimental detection of false data injection attacks, this article will conduct a detailed analysis of the power system segmentation. This paper comprehensively considers the similarity of nodes, security control and confidentiality strategies to complete the optimal partition. Next, we preprocess the measurement data. In order to improve the adaptability of the algorithm structure to the grid structure and further improve the accuracy and convergence speed of the algorithm output, a differential evolution algorithm with swarm intelligence is proposed. Obtain a higher-precision state estimate useful for detecting bad data. In the experiment of this article, if false data account for 20% of all data, the detection accuracy exceeds 75%. As the number of experimental groups increases, the detection accuracy of only one type of false data that does not meet the rules will continue to increase, but the detection accuracy of other types of false data will not change much, but the overall detection accuracy will become higher. Experimental results show that the detection framework can not only effectively detect and identify false data injection attacks on multiple bus nodes, but also has high detection accuracy and can effectively recover false data.

In the recent years, cyberattacks to smart grids are becoming more frequent. Among the many malicious activities that can be launched against smart grids, the False Data Injection (FDI) attacks have raised significant concerns from both academia and industry. FDI attacks can affect the (internal) state estimation process—critical for smart grid monitoring and control—thus being able to bypass conventional Bad Data Detection (BDD) methods. Hence, prompt detection and precise localisation of FDI attacks are becoming of paramount importance to ensure smart grids security and safety. Several papers recently started to study and analyse this topic from different perspectives and address existing challenges. Data‐driven techniques and mathematical modelling are the major ingredients of the proposed approaches. The primary objective is to provide a systematic review and insights into FDI attacks joint detection and localisation approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localisation aspects. For this purpose, more than 40 major research contributions were selected and inspected, while conducting a detailed analysis of the methodology and objectives in relation to the FDI attacks detection and localisation. Key findings of the identified papers were provided according to different criteria, such as employed FDI attacks localisation techniques, utilised evaluation scenarios, investigated FDI attack types, application scenarios, adopted methodologies and the use of additional data. Finally, open issues and future research directions were discussed.

Nowadays, the increase in electrical energy consumption and power system restructuring have posed new challenges to the operation, control, and monitoring of power systems. In this situation, the supervisory control and data acquisition (SCADA) system is not enough to ensure power system security and stability. The SCADA is often unable to measure data of all buses simultaneously. In addition, the sampling rate in this system is not enough for some power system applications. Therefore, the information obtained from SCADA does not show power system dynamics properly. In order to improve the power system monitoring, wide area measurement system (WAMS) has been developed to overcome the problems of SCADA system. Phasor measurements units (PMUs) are the main part of WAMS system and it basically consists of three essential processes including collecting, transmitting, and analyzing data. WAMS receives obtained data via a high speed communication links. After data processing and extracting appropriate information, decisions are made to improve the power system performance. Efficient use of power system data to achieve a secure operation strategy is targeted using the WAMS system. Due to the effective role of WAMS and PMUs in the reliable operation of power system, it is necessary to study their concept and applications in this chapter. The history of PMU and its structure is presented in this chapter. In addition, the necessity of WAMS for future power system and its difference from SCADA system have been investigated in this chapter. Different algorithms and application of the WAMS are also introduced in this chapter which can be implemented to improve the performance of the future power system.

The load frequency control (LFC) system is an essential component of the smart grid and is located between cyberspace information and the physical infrastructure. Because the operation of LFC depends on the modern network communication technology, it is vulnerable to network attacks. The false data injection attack (FDIA) injects attack data into the data collected by the LFC system, affecting the control center's calculation and causing errors in the control signals, which affects the stable operation of the power system. For the LFC system, this article builds a cyber-physical model. Based on the model, an FDIA detection and defense mechanism based on the GAN network is proposed. By calculating, contrasting, and replacing the control signals, the proposed method can detect and defend against FDIAs. The experimental results on the two-area interconnected power system, the IEEE 39-bus, and IEEE 118-bus power system show that the proposed defense method can ensure the power system frequency and tie-line power deviation remain within the set value when suffered from FDIA.