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.

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%.

基于反向鲸鱼-多隐层极限学习机的电网FDIA检测

针对目前已有的电力信息物理系统虚假数据注入攻击检测方法由于特征表达能力有限,而导致无法精确获取受攻击位置的问题,提出一种基于反向学习鲸鱼优化多隐层极限学习机的虚假数据注入攻击定位检测方法。所提方法不仅将极限学习机拓展为多隐层神经网络,解决其特征表达能力有限的问题,而且引入鲸鱼优化算法对多隐层极限学习机的各隐层神经元个数进行寻优并采用反向学习策略提高其收敛速度和检测精度,以防止随机确定各隐层神经元个数对检测方法的泛化性能和定位检测结果造成影响。通过在不同场景下对IEEE-14和57节点测试系统进行大量实验,验证了所提方法能够通过历史数据自动识别受攻击的系统状态量所对应的精确位置。与其他多种方法相比,所提方法具有更优的精度、召回率和F1值。

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 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.

FDI攻击下移动机器人弹性预测镇定控制研究

提出一种具有自适应预测时域的输入重构弹性自触发模型预测控制(self-triggered model predictive control,ST-MPC)算法,平衡机器人系统网络安全和资源受限之间的矛盾.首先,基于自触发非周期采样特征和虚假数据注入(false data injection,FDI)攻击模型设计输入重构机制,确保机器人系统可快速重构,能削弱FDI攻击影响的可行控制序列.其次,结合输入重构机制设计关键数据选取条件和预测时域调节机制,从实现最大化触发间隔和降低优化问题复杂度两个方面降低资源消耗.然后,基于输入重构和预测时域调节机制设计弹性ST-MPC镇定控制算法,并推导FDI攻击下算法的可行性和闭环系统稳定性条件.最后,通过仿真实验验证所提出算法能够在抵御FDI攻击前提下保持较好的控制性能及资源利用率.

基于博弈论的信息物理系统在FDI攻击下的控制

随着信息物理系统(Cyber-Physical System,CPS)的广泛应用,很多恶意攻击者都将注意力转移到了CPS上.针对存在虚假数据注入(False Data Injection,FDI)攻击的信息物理系统,从控制理论角度入手,以非合作博弈的二人零和博弈为基础设计H∞鲁棒控制方法,将控制器和攻击信号分别视为博弈双方参与者,通过寻找二人零和博弈的纳什均衡点从而保证最坏攻击情况下系统的稳定运行.在此基础上,提出了一种无模型Q-学习算法,在不需要系统动力学信息的情况下在线学习最优控制策略.最后进行了仿真实验,验证所提方法的有效性.

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.

基于拉普拉斯特征映射学习的隐匿FDI攻击检测

智能电网中的隐匿虚假数据入侵(False data injection,FDI)攻击能够绕过坏数据检测机制,导致控制中心做出错误的状态估计,进而干扰电力系统的正常运行.由于电网系统具有复杂的拓扑结构,故基于传统机器学习的攻击信号检测方法存在维度过高带来的过拟合问题,而深度学习检测方法则存在训练时间长、占用大量计算资源的问题.为此,针对智能电网中的隐匿FDI攻击信号,提出了基于拉普拉斯特征映射降维的神经网络检测学习算法,不仅降低了陷入过拟合的风险,同时也提高了隐匿FDI攻击检测学习算法的泛化能力.最后,在IEEE57-Bus电力系统模型中验证了所提方法的优点和有效性.

基于蠕虫传播和FDI的电力信息物理协同攻击策略

随着信息技术与现代电力系统的结合日趋紧密,通信系统异常和网络攻击均可能影响到电力系统的安全稳定运行.为了研究工控蠕虫病毒对电网带来的安全隐患,本文首次建立了基于马尔科夫决策过程(Markov decision process,MDP)的电力信息物理系统跨空间协同攻击模型,该模型同时考虑通信设备漏洞被利用的难易程度为代价以及对电力网络的破坏程度为收益两方面因素,能够更有效地识别系统潜在风险.其次,采用Q学习算法求解在该模型下的最优攻击策略,并依据电力系统状态估计的误差值来评定该攻击行为对电力系统造成的破坏程度.最后,本文在通信8节点-电力14节点的耦合系统上进行联合仿真,对比结果表明相较单一攻击方式,协同攻击对电网的破坏程度更大.与传统的不考虑通信网络的电力层攻击研究相比,本模型辨识出的薄弱节点也考虑了信息层的关键节点的影响,对防御资源的分配有指导作用.

Detection of false data injection attacks using unscented Kalman filter

It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems(EMSs), is vulnerable to false data injection attacks due to the undetectability of those attacks using standard bad data detection techniques,which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter(UKF) in conjunction with a weighted least square(WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.

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.

基于自适应卡尔曼滤波的FDIA检测方法

针对信息物理系统下的虚假数据注入攻击(False Data Injection Attack, FDIA)中的随机攻击和隐蔽攻击,基于自适应卡尔曼滤波研究了攻击检测问题。常用的卡方检测可以有效检测出FDIA中的随机攻击,但是具有隐蔽性的FDIA可以绕过错误数据检测机制,使得卡方检测失败。由此在卡方检测的基础上结合相似性检测,针对系统噪声的时变特性,基于自适应卡尔曼滤波提出新的检测方法。该算法解决了实际噪声不确定性对系统的影响,且能有效检测FDIA中的随机攻击和隐蔽攻击。通过仿真验证了该方法的有效性。

FDI攻击下自触发预测控制安全策略研究

针对信息物理融合系统自触发模型预测控制在虚假数据注入(FDI)攻击下的安全控制问题,提出一种基于控制信息重构的安全控制策略。首先,在控制器端对连续控制信号进行离散采样,并对少数关键数据进行重点加密;其次,在网络端传输离散化后的控制信号样本,并在被控系统处基于预设模式对控制信号进行重构;然后,推导了在系统遭受虚假数据注入攻击之时,重点加密数据的选取条件,并在理论上严格证明了基于重点加密数据重构获得的控制信号能够保证控制系统的可行性与稳定性;最后,基于机器人系统进行了仿真实验,验证了算法的有效性。

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.

Graph-theoretic defense mechanisms against false data injection attacks in smart grids

This paper addresses false data injection, which is one of the most significant security challenges in smart grids. Having an accurately estimated state is of great importance for maintaining a stable running condition of smart grids. To preserve the accuracy of the estimated state, bad data detection(BDD) mechanisms are utilized to remove erroneous measurements due to meter failures or outside attacks. In this paper we use a graph-theoretic formulation for false data injection attacks in smart grids and propose defense mechanisms to mitigate this type of attacks. To this end, we discuss characteristics of a typical smart grid graph such as planarity. Then we propose three different approaches to find optimal protected meters set: a fast and efficient heuristic algorithm that works well in practice, an approximation algorithm that provides guarantee for the quality of the protected set, and an exact algorithm that finds the optimal solution. Our extensive simulation results show that our algorithms outperform similar existing solutions in terms of different performance metrics.

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.