Excluding compromised node by tracing false data injected messages in Wireless Sensor Network

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.

False data injection (FDI) attacks are malicious insertions of false data as sensor measurements in a cyber-physical system, in order to lead the system to take a wrong action. False data injection attacks do not attack the computational or network components of cyber-physical systems but the interface between the physical and the cyber part. Such attacks are powerful and can have catastrophic results. Defense against them can be achieved by limiting the attack surface through vulnerability analysis of the cyber-physical system design and by monitoring system operation in the field with monitors that observe system parameters and sensor measurements and detect abnormal operation early. In this chapter, we describe promising techniques for vulnerability analysis and dynamic monitoring, based on efficient SMT solvers and Kalman filter techniques, respectively.

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.

The pervasiveness of information and communications technologies as well as intelligent electronic devices leads to an expanded attack surface in smart grids, making it increasingly challenging to withstand the high-profile false data injection (FDI) attacks. In this paper, we propose a Proactive Approach to Mitigate FDI Attacks (PAMA) in smart grids. With PAMA scheme, the critical information - power grid connections and configurations as well as the original measurement data - used for constructing FDI attacks is well protected from leakage or theft, so that FDI attacks are effectively mitigated. Specifically, we transform the state estimation and FDI detection application into a distributed one equipped with converted information from the critical information provided by the control center. In addition, the original measurement data is also protected by using a secure hybrid Paillier cryptosystem. Our PAMA scheme is proved to be secure and effective in mitigating FDI attacks on smart grids. The computational complexity and the communication overhead are evaluated on the standard IEEE 14-bus test system. Keywords <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">__</sup> Smart grids, false data injection (FDI) attack, Paillier cryptosystem, state estimation.

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.

False data injection attacks with complete stealthiness in cyber–physical systems: A self-generated approach

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.

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.

The cybersecurity of the smart grid is becoming a critical issue to the power systems. Mitigating potential cyberattacks on the control of the power electronic devices is an important concern in the controller design to enhance the resilience of the overall power grid. In this paper, a novel model predictive control (MPC) is proposed for the multilevel cascaded H-bridge inverters considering the false data injection (FDI) attacks. The FDI attack is a vicious attack scenario when the attack signals are injected into the sensors in the cyberphysical systems (CPS). It is shown that FDI signals can effectively penetrate the commonly used Kalman Filter (KF) based state estimation and jeopardize the control of the inverters. The k-Nearest Neighbors (KNN) algorithm is employed in the proposed scheme to mitigate the FDI attacks. The proposed KNN-based MPC scheme was tested with various types of attack signals. The results show that the proposed scheme exhibits a robust performance in the face of the malicious FDI attacks.

In this paper, we consider false data injection (FDI) attacks with limited information of transmission-line susceptances and new countermeasures in smart grids. First, we prove that the adversary could launch FDI attacks to modify the state variable on a bus or superbus only if he/she knows the susceptance of every transmission line that is incident to that bus or superbus. Based on this observation, we provide a new countermeasure against FDI attacks, i.e., to make the susceptances of n-1 interconnected transmission lines that cover all buses unknown to the adversary (e.g., by proactively perturbing transmission-line susceptances through distributed flexible AC transmission system (D-FACTS) devices), where n is the total number of buses. This new countermeasure can work alone or in conjunction with traditional ones to reduce the number of meter measurements/state variables that are to be secured against FDI attacks. The implementation of FDI attacks with limited susceptance information and the effectiveness of new countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.

Design of False Data Injection Attacks in Cyber-Physical Systems

This paper studies the secure state estimation problem for multi-area power systems with unknown load deviation when the sensor measurements of power systems encounter false data injection (FDI) attacks. By modeling a new state-space model including the variables of load deviation and FDI attack signals, an intermediate observer-based method is developed to simultaneously estimate the load deviation and FDI attacks. Then, the secure state estimator is designed by compensating FDI attacks, while the unknown load deviation in power systems is compensated by its estimated values to eliminate system oscillations as much as possible. Simulations show the effectiveness of the proposed methods.

This paper studies the cooperative output regulation problem for heterogeneous multi‐agent systems (MASs) subject to false data injection (FDI) attacks, which means the malicious cyber‐attackers may inject false data into the communication topology randomly. To ensure that each follower agent can track the leader agent accurately, we construct both an observer and a control law for each follower agent. Since the FDI attacks may exist in the communication network, a filter with an adaptive compensator, which can estimate attack signals dynamically, is equipped for each observer, such that the filter can neutralize attacks when malicious attackers launch attacks. Therefore, the cooperative output regulation can be achieved under the control law based on the observer no matter whether there exist attacks or not. Finally, a numerical example is provided for validating the theoretical results.

Wireless Sensor Networks (WSNs) are used in many applications in military, environmental, and health-related areas. These applications often include the monitoring of sensitive information such as enemy movement on the battlefield or the location of personnel in a building. Security is important in WSNs. However, WSNs suffer from many constraints, including low computation capability, small memory, limited energy resources, susceptibility to physical capture, and the use of insecure wireless communication channels. These constraints make security in WSNs a challenge. In this paper, we try to explore security issue in WSN. First, the constraints, security requirements and attacks with their corresponding countermeasures in WSNs are explained. Individual sensor nodes are subject to compromised security. An adversary can inject false reports into the networks via compromised nodes. Furthermore, an adversary can create a Gray hole by compromised nodes. If these two kinds of attacks occur simultaneously in a network, some of the existing methods fail to defend against those attacks. The Ad-hoc On Demand Distance (AODV) Vector scheme for detecting Gray-Hole attack and Statistical En-Route Filtering is used for detecting false report. For increasing security level, the Elliptic Curve Cryptography (ECC) algorithm is used. Simulations results obtain so far reduces energy consumption and also provide greater network security to some extent.

False data injection (FDI) attacks have recently been introduced as an important class of cyber-attacks against power system state estimation. Utilizing vulnerabilities in information systems, attackers can inject well-constructed false data and stealthily misguide results of state estimation. However, only measurements, such as power flows and bus injections, are coordinately modified in FDI attacks, which may result in a larger number of modified measurements in constructing such attacks. In this article, we propose a network parameter coordinated false data injection (NP-FDI) attack to reduce the number of attacked measurements, where expected changes of system states and modifications of network parameters are well coordinated. Analysis of minimal attack set at a single line gives feasible conditions in reducing the number of attacked measurements. A sparse attack strategy is designed to obtain the minimal attack set of the whole grid, which can also be applied to cases with incomplete topology information. An extension to NP-FDI attacks with incomplete line impedance is presented, where the required line impedance can be estimated from local measurements adjacent to the targeted branch. Based on simulations in the IEEE 14-bus and IEEE 118-bus test systems, performance of the proposed NP-FDI attacks on sparsity and stealth are evaluated.