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.

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%.

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.

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.

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.

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.

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.

基于RNN的智能电网拓扑变异型FDI攻击检测方法

针对近年来对智能电网运行状态构成严重安全威胁的虚假数据注入问题,提出一种基于循环神经网络的智能电网拓扑变异型虚假数据注入攻击检测方法.通过分析电力系统状态估计方法的不足和虚假数据注入攻击绕过系统监测与防御的入侵方式,引入循环神经网络分析连续数据序列的时序变化,并在IEEE-30节点系统上进行仿真验证.仿真结果表明,提出的方法能够高效、准确地检测智能电网中产生的虚假数据注入攻击行为,其检测准确率可达99.9%,相比于其他检测方法具有较大的优势.

面向电力SCADA系统的FDIA检测方法综述

信息通信技术的发展和智能设备的引入使电力系统逐渐演变为电力信息物理系统,而信息层与物理层之间的深度耦合也加剧了电力系统遭受网络攻击的风险。虚假数据注入攻击(false data injection attack,FDIA)作为一种兼具隐蔽性、灵活性和攻击导向性的网络攻击方式,对电力数据采集与监控(supervisory control and data acquisition,SCADA)系统的安全稳定构成很大威胁。为应对这一威胁挑战,学者们研究了各种各样的FDIA检测方法。该文对面向电力SCADA系统的FDIA检测方法进行综述,首先介绍了FDIA的攻击原理及构建方法,梳理了FDIA检测算法的发展历程,并按照模型驱动和数据驱动对算法进行了分类整理,针对模型驱动中的基于状态估计、图论、物理特性等检测方法和数据驱动中的有监督学习、无监督学习、半监督学习、对抗博弈学习和强化学习等检测方法分别进行了机理分析;然后对比分析了相关算法的检测性能、优缺点及其适用场景;最后,对FDIA检测防御的后续研究方向进行了展望。

基于最大似然估计的智能电网FDIA检测

智能电网的正常运行依赖于准确反映电网物理特性的状态估计。针对虚假数据注入攻击(False Data Injection Attack,FDIA)通过向电力系统量测单元注入恶意数据来篡改状态估计结果的问题,提出了一种基于最大似然估计(Maximum Likelihood Estimation,MLE)的电网FDIA检测方法,并以此提高状态估计结果的精度。首先,基于智能电网量测向量与FDIA攻击向量服从具有不同协方差多元高斯分布的特点,通过MLE计算法求得量测数据期望与协方差,根据该协方差判断是否存在虚假量测数据。其次,若数据正常,通过加权最小二乘(Weighted Least Square,WLS)算法依据该量测数据期望进行状态估计可以得到更加优秀的系统状态结果。最后,基于IEEE-14节点系统的算例证明了该算法的可行性。

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.

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.

基于卡尔曼滤波的直流微电网抵御FDI攻击的二次控制策略

针对微电网物理层和信息层交互过程中存在虚假数据注入(FDI)攻击的情况,提出了基于卡尔曼滤波的直流微电网抵御FDI攻击的二次控制策略。考虑常数FDI攻击和时变FDI攻击,利用卡尔曼滤波器滤除二次控制中引入的攻击信号,将经过滤波后的二次控制信号输入一次控制中,实现恢复电压和精确分配功率的目的。通过2个仿真案例验证了所提方法的有效性。

基于相关特征-多标签级联提升森林的电网虚假数据注入攻击定位检测

虚假数据注入攻击严重威胁了电网安全稳定运行。由于电力量测数据维度高、特征复杂,传统攻击定位检测方法存在定位精度不足的问题。为此,提出一种基于相关特征-多标签级联提升森林的电网虚假数据注入攻击定位检测方法来精确定位电网受攻击的位置。所提方法通过融入极端梯度提升算法来增强多标签级联森林对复杂电力量测数据的拟合能力,进而识别系统各节点状态量的异常;引入“相关特征”算法来对原始电力量测数据中的高信息性特征进行提取,提升多标签级联森林的泛化能力,以获得更精确的定位检测。在IEEE-14和IEEE-57节点系统中进行仿真测试,验证了所提方法的有效性,且与其他方法相比,所提方法具有更优的准确率、查准率、灵敏度和F1分数。

基于多智能体遗传算法的云平台抗虚假数据注入攻击方法

为确保云平台内数据传输安全,提出一种基于多智能体遗传算法的云平台抗虚假数据注入攻击方法。采用开源平台OpenStack搭建云平台,并分析云平台虚假数据注入攻击过程;以该攻击过程为基础,结合Copula函数与GAN生成对抗网络构建虚假数据注入攻击检测框架,利用Copula GAN函数模型中的判别器与生成器对云平台原始量测数据进行对抗训练,再采用极端随机树分类器检测虚假数据,判断云平台中是否存在虚假数据注入攻击情况;利用三层攻防博弈模型防御云平台中的虚假数据注入攻击,同时由该模型为各条数据传输线路分配防御资源,并设置对应的约束条件;采用多智能体遗传算法对模型进行优化求解,完成云平台虚假数据注入攻击目标防御。实验结果表明,该方法可以精准检测云平台虚假数据并及时采取防御措施,具备较强的抗虚假数据注入攻击能力。

面向电力数据攻击的无监督机器学习入侵检测方法

针对电力系统的虚假数据注入攻击,研究基于无监督机器学习的电力入侵检测方法。首先,构建稀疏且隐蔽的虚假数据注入攻击(False Data Injection Attacks,FDIAs)模型,得到优化后的FDIAs;其次,采用孤立森林、隐马尔可夫模型两种无监督机器学习算法构建入侵检测框架,分别通过构建二叉树和孤立树、状态序列的预测等实现电力系统的FDIAs入侵检测;再次,采用合理的性能指标全面地对入侵检测性能进行评估;最后,与极端梯度提升、随机森林等监督机器学习算法进行对比实验,基于IEEE电力系统平台验证基于孤立森林等无监督机器学习入侵检测算法的优劣。实验结果表明:无监督机器学习算法可自动从数据中发现特征,相对于基于极端梯度提升、随机森林的入侵检测方法的失效,基于孤立森林的入侵检测方法在无标签数据的前提下,其综合F1-score仍达到0.9942。

基于MGAT-TCN模型的可解释电网虚假数据注入攻击检测方法

新型电力系统背景下,快速、准确的虚假数据注入攻击(FDIA)检测对电网安全运行至关重要。但现有深度学习方法未能充分挖掘电网量测数据的时序和空间特征信息,影响了模型的检测性能;同时,深度神经网络的“黑盒”属性降低了检测模型的可解释性,导致检测结果缺乏可信度。针对上述问题,提出了一种基于多头图注意力网络和时间卷积网络(MGAT-TCN)模型的可解释电网FDIA检测方法。首先,考虑电网拓扑连接关系与量测数据的空间相关性,引入空间拓扑感知注意力机制,建立多头图注意力网络(MGAT)提取量测数据的空间特征;接着,利用时间卷积网络(TCN)并行提取量测数据的时序特征;最后,在IEEE 14节点系统和IEEE 39节点系统中对所提MGAT-TCN模型进行仿真验证。结果表明,所提模型相比于现有检测模型具有更高的检测准确率和效率,且通过拓扑热力图对注意力权值可视化,实现了模型在空间维度的可解释性。