Residual-Based False Data Injection Attacks Against Multi-Sensor Estimation Systems

This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.

Passivity-Based Robust Control Against Quantified False Data Injection Attacks in Cyber-Physical Systems

Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control signals.For quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion perspective.Then,we design an energy controller to dynamically adjust the system energy changes caused by unknown attacks.The designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping injection.Moreover,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack observer.In addition,this secure control method is simple to implement because it avoids complicated mathematical operations.The effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.

Detection and Defense Method Against False Data Injection Attacks for Distributed Load Frequency Control System in Microgrid

In the realm of microgrid(MG),the distributed load frequency control(LFC)system has proven to be highly susceptible to the negative effects of false data injection attacks(FDIAs).Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG,this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system.Firstly,the method integrates a bi-directional long short-term memory(Bi LSTM)neural network and an improved whale optimization algorithm(IWOA)into the LFC controller to detect and counteract FDIAs.Secondly,to enable the Bi LSTM neural network to proficiently detect multiple types of FDIAs with utmost precision,the model employs a historical MG dataset comprising the frequency and power variances.Finally,the IWOA is utilized to optimize the proportional-integral-derivative(PID)controller parameters to counteract the negative impacts of FDIAs.The proposed detection and defense method is validated by building the distributed LFC system in Simulink.

Adaptive security control of time-varying constraints nonlinear cyber-physical systems with false data injection attacks

In this article,an adaptive security control scheme is presented for cyber-physical systems(CPSs)suffering from false data injection(FDI)attacks and time-varying state constraints.Firstly,an adaptive bound estimation mechanism is introduced in the backstepping control design to mitigate the effect of FDI attacks.Secondly,to solve the unknown sign time-varying statefeedback gains aroused by the FDI attacks,a type of Nussbaum function is employed in the adaptive security control.Then,by constructing a barrier Lyapunov function,it can be ensured that all signals of controlled system are bounded and the time-varying state constraints are not transgressed.Finally,the provided simulation examples demonstrate the effectiveness of the proposed controller.

Detection of false data injection attacks on power systems using graph edge-conditioned convolutional networks

State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.

Analysis of Stealthy False Data Injection Attacks Against Networked Control Systems:Three Case Studies

This paper mainly investigates the security problem of a networked control system based on a Kalman filter.A false data injection attack scheme is proposed to only tamper the measurement output,and its stealthiness and effects on system performance are analyzed under three cases of system knowledge held by an attacker and a defender.Firstly,it is derived that the proposed attack scheme is stealthy for a residual-based detector when the attacker and the defender hold the same accurate system knowledge.Secondly,it is proven that the proposed attack scheme is still stealthy even if the defender actively modifies the Kalman filter gain so as to make it different from that of the attacker.Thirdly,the stealthiness condition of the proposed attack scheme based on an inaccurate model is given.Furthermore,for each case,the instability conditions of the closed-loop system under attack are derived.Finally,simulation results are provided to test the proposed attack scheme.

A Privacy-preserving Algorithm for AC Microgrid Cyber-physical System Against False Data Injection Attacks

A new privacy-preserving algorithm based on the Paillier cryptosystem including a new cooperative control strategy is proposed in this paper, which can resist the false data injection(FDI) attack based on the finite-time control theory and the data encryption strategy. Compared with the existing algorithms, the proposed privacy-preserving algorithm avoids the direct transmission of the ciphertext of frequency data in communication links while avoiding complex iterations and communications. It builds a secure data transmission environment that can ensure data security in the AC microgrid cyber-physical system(CPS). This algorithm provides effective protection for AC microgrid CPS in different cases of FDI attacks. At the same time, it can completely eliminate the adverse effects caused by the FDI attack. Finally, the effectiveness, security, and advantages of this algorithm are verified in the improved IEEE 34-node test microgrid system with six distributed generators(DGs) in different cases of FDI attacks.

Active resilient defense control against false data injection attacks in smart grids

The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.

Security control of Markovian jump neural networks with stochastic sampling subject to false data injection attacks

The security control of Markovian jumping neural networks(MJNNs)is investigated under false data injection attacks that take place in the shared communication network.Stochastic sampleddata control is employed to research the exponential synchronization of MJNNs under false data injection attacks(FDIAs)since it can alleviate the impact of the FDIAs on the performance of the system by adjusting the sampling periods.A multi-delay error system model is established through the input-delay approach.To reduce the conservatism of the results,a sampling-periodprobability-dependent looped Lyapunov functional is constructed.In light of some less conservative integral inequalities,a synchronization criterion is derived,and an algorithm is provided that can be solved for determining the controller gain.Finally,a numerical simulation is presented to confirm the efficiency of the proposed method.

False data injection attacks against smart grid state estimation:Construction, detection and defense

As a typical representative of the so-called cyber-physical system,smart grid reveals its high efficiency,robustness and reliability compared with conventional power grid.However,due to the deep integration of electrical components and computinginformation in cyber space,smart grid is vulnerable to malicious attacks,especially for a type of attacks named false data injection attacks(FDIAs).FDIAs are capable of tampering meter measurements and affecting the results of state estimation stealthily,which severely threat the security of smart grid.Due to the significantinfluence of FDIAs on smart grid,the research related to FDIAs has received considerable attention over the past decade.This paper aims to summarize recent advances in FDIAs against smart grid state estimation,especially from the aspects of background materials,construction methods,detection and defense strategies.Moreover,future research directions are discussed and outlined by analyzing existing results.It is expected that through the review of FDIAs,the vulnerabilities of smart grid to malicious attacks can be further revealed and more attention can be devoted to the detection and defense of cyber-physical attacks against smart grid.

Impact analysis of false data injection attacks on power system static security assessment

Static security assessment(SSA) is an important procedure to ensure the static security of the power system.Researches recently show that cyber-attacks might be a critical hazard to the secure and economic operations of the power system. In this paper, the influences of false data injection attack(FDIA) on the power system SSA are studied. FDIA is a major kind of cyber-attacks that can inject malicious data into meters, cause false state estimation results, and evade being detected by bad data detection. It is firstly shown that the SSA results could be manipulated by launching a successful FDIA, which can lead to incorrect or unnecessary corrective actions. Then,two kinds of targeted scenarios are proposed, i.e., fake secure signal attack and fake insecure signal attack. The former attack will deceive the system operator to believe that the system operates in a secure condition when it is actually not. The latter attack will deceive the system operator to make corrective actions, such as generator rescheduling, load shedding, etc. when it is unnecessary and costly. The implementation of the proposed analysis is validated with the IEEE-39 benchmark system.

Detection and Estimation of False Data Injection Attacks for Load Frequency Control Systems

False data injection attacks(FDIAs)against the load frequency control(LFC)system can lead to unstable operation of power systems.In this paper,the problems of detecting and estimating the FDIAs for the LFC system in the presence of external disturbances are investigated.First,the LFC system model with FDIAs against frequency and tie-line power measurements is established.Then,a design procedure for the unknown input observer(UIO)is presented and the residual signal is generated to detect the FDIAs.The UIO is designed to decouple the effect of the unknown external disturbance on the residual signal.After that,an attack estimation method based on a robust adaptive observer(RAO)is proposed to estimate the state and the FDIAs simultaneously.In order to improve the performance of attack estimation,the H¥technique is employed to minimize the effect of external disturbance on estimation errors,and the uniform boundedness of the state and attack estimation errors is proven using Lyapunov stability theory.Finally,a two-area interconnected power system is simulated to demonstrate the effectiveness of the proposed attack detection and estimation algorithms.

Blind false data injection attacks in smart grids subject to measurement outliers

False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,which eliminate the limitation of grasping measurement Jacobian matrix H in advance,but when there are outliers in measurement data,attack performance is degraded.In this paper,improved BFDIAs are proposed.In off-line phase,lowdimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm(LLTSA)is sent into Continuous Deep Belief Network(CDBN)as training data to learn their probability distribution.In on-line phase,real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs,and outputs are reconstructed by the probability distribution in off-line phase,which eliminates the influence of outliers indirectly.Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.

Kinematic Control of Serial Manipulators Under False Data Injection Attack

With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits can be achieved with such a configuration,it also brings the concern of cyber attacks to the industrial control systems,such as networked manipulators that are widely adopted in industrial automation.For such systems,a false data injection attack on a control-center-to-manipulator(CC-M)communication channel is undesirable,and has negative effects on the manufacture quality.In this paper,we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model.Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack.The efficacy of the proposed method is validated via simulations.

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

Detection and isolation of false data injection attack via adaptive Kalman filter bank

Due to the integration of cyber–physical systems,smart grids have faced the new security risks caused by false data injection attacks(FDIAs).FDIAs can bypass the traditional bad data detection techniques by falsifying the process of state estimation.For this reason,this paper studies the detection and isolation problem of FDIAs based on the adaptive Kalman filter bank(AKFB)in smart grids.Taking the covert characteristics of FDIAs into account,a novel detection method is proposed based on the designed AKF.Moreover,the adaptive threshold is proposed to solve the detection delay caused by a priori threshold in the current detection methods.Considering the case of multiple attacked sensor nodes,the AKFB-based isolation method is developed.To reduce the number of isolation iterations,a logical decision matrix scheme is designed.Finally,the effectiveness of the proposed detection and isolation method is demonstrated on an IEEE 22-bus smart grids.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal selfattention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

SAC-TA: A Secure Area Based Clustering for Data Aggregation Using Traffic Analysis in WSN

Clustering is the most significant task characterized in Wireless Sensor Networks (WSN) by data aggregation through each Cluster Head (CH). This leads to the reduction in the traffic cost. Due to the deployment of the WSN in the remote and hostile environments for the transmission of the sensitive information, the sensor nodes are more prone to the false data injection attacks. To overcome these existing issues and enhance the network security, this paper proposes a Secure Area based Clustering approach for data aggregation using Traffic Analysis (SAC-TA) in WSN. Here, the sensor network is clustered into small clusters, such that each cluster has a CH to manage and gather the information from the normal sensor nodes. The CH is selected based on the predefined time slot, cluster center, and highest residual energy. The gathered data are validated based on the traffic analysis and One-time Key Generation procedures to identify the malicious nodes on the route. It helps to provide a secure data gathering process with improved energy efficiency. The performance of the proposed approach is compared with the existing Secure Data Aggregation Technique (SDAT). The proposed SAC-TA yields lower average energy consumption rate, lower end-to-end delay, higher average residual energy, higher data aggregation accuracy and false data detection rate than the existing technique.

Optimal Hybrid Deep Learning Enabled Attack Detection and Classificationin IoT Environment

The Internet of Things (IoT) paradigm enables end users to accessnetworking services amongst diverse kinds of electronic devices. IoT securitymechanism is a technology that concentrates on safeguarding the devicesand networks connected in the IoT environment. In recent years, False DataInjection Attacks (FDIAs) have gained considerable interest in the IoT environment.Cybercriminals compromise the devices connected to the networkand inject the data. Such attacks on the IoT environment can result in a considerableloss and interrupt normal activities among the IoT network devices.The FDI attacks have been effectively overcome so far by conventional threatdetection techniques. The current research article develops a Hybrid DeepLearning to Combat Sophisticated False Data Injection Attacks detection(HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD modelmajorly recognizes the presence of FDI attacks in the IoT environment.The HDL-FDIAD model exploits the Equilibrium Optimizer-based FeatureSelection (EO-FS) technique to select the optimal subset of the features.Moreover, the Long Short Term Memory with Recurrent Neural Network(LSTM-RNN) model is also utilized for the purpose of classification. At last,the Bayesian Optimization (BO) algorithm is employed as a hyperparameteroptimizer in this study. To validate the enhanced performance of the HDLFDIADmodel, a wide range of simulations was conducted, and the resultswere investigated in detail. A comparative study was conducted between theproposed model and the existing models. The outcomes revealed that theproposed HDL-FDIAD model is superior to other models.

Defense of Massive False Data Injection Attack via Sparse Attack Points Considering Uncertain Topological Changes

False data injection attack(FDIA)is a typical cyber-attack aiming at falsifying measurement data for state estimation(SE),which may incur catastrophic consequences on cyber-physical system operation.In this paper,we develop a deep learning based methodology for detection,localization,and data recovery of FDIA on power systems in a coherent and holistic manner.However,the multi-modal probability distributions of both measurements and state variables in SE due to ever-changing operating points and structural/topological changes pose great challenges in detecting and localizing FDIA.To address this challenge,we first propose an enhanced attack model to launch massive FDIA on limited access points.Second,we train an auto-encoder(AE)with a Bayesian change verification(BCV)classifier using N-1 contingencies to detect FDIA with unseen N-k operational topologies.Third,to avoid model collapse caused by multi-modal measurement distribution,an AE-based generative adversarial network(GAN)is derived to generate a diverse candidate set of normal measurement vectors with various operational topologies.Finally,we develop a pattern match algorithm to localize and recover the falsified measurements and state variables by comparing the falsified measurement vectors with the normal measurement vectors in the candidate set.Case studies with IEEE benchmark systems and a modified 415-bus China Southern Grid system are provided to validate the proposed methodology.It shows that the proposed methodology achieves an average 95%accuracy for detection,over 80%accuracy for localization of FDIA,and recovers the measurement and state variables close to their true values.