False Data Injection Attack for Cyber-Physical Systems With Resource Constraint

Shifting Attack Stabilization and Estimation of Hidden Markov Boolean Networks

In this article, a network attack, named shifting attack, is considered for hidden Markov Boolean control networks (HMBCNs). Using semi-tensor product of matrices, the considered network and the network attack are presented in algebraic form. State feedback control (SFC) is then applied to stabilize the considered network to a desired state. A necessary and sufficient condition based on the probability matrix is presented for the stochastic stabilization of the HMBCN, based on which, the design of the SFC is given. Then shifting attack is further studied for HMBCNs. The control strategy will be shifted to another when the HMBCN is under attack. Shifting attack is also modeled in a hidden Markov process, under which, a necessary and sufficient condition for the security of the attacked HMBCN is also presented. Propositions are obtained for the security and insecurity of the attacked HMBCN. Using the change of the probability measurement approach, estimations of the states expectation and the attacked signals expectation for the attacked HMBCN are solved. At last, examples show the effectiveness of the obtained results.

Extended state observer-based backstepping control for nonlinear systems under FDI attacks

In this paper, a novel extended state observer-based backstepping control scheme is proposed for strict-feedback nonlinear systems with unmeasured states suffering from false data injection (FDI) attacks. An extended state observer is designed to achieve simultaneous online estimation of system states and FDI attacks. A secure output feedback tracking control scheme with an attack compensation method is proposed to reduce the influence of FDI attacks. It is proven that the proposed scheme can guarantee that the closed-loop system is semi-global uniformly ultimately bounded. Moreover, it is shown that the observation errors can be as small as desired with an adjustable parameter and the tracking error can converge to a small neighborhood of the origin. Finally, two simulation examples verify the efficiency of the proposed approach.

Joint Adversarial Example and False Data Injection Attacks for State Estimation in Power Systems

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.

Stealthy false data injection attacks with resource constraints against multi-sensor estimation systems

This paper mainly investigates how to maximally degrade estimation performance of a cyber-physical system under limited resource. A stealthy false data injection (FDI) attack scheme is proposed to only attack partial sensor channels of a multi-sensor estimation system. The attack stealthiness condition and the compromised estimation error covariance are respectively derived, and then the stealthy attack problem is formed as a constrained optimization problem. An explicit solution of the optimal attack strategy is given and proven. Furthermore, the relationship between the compromised estimation error covariance and the attacked sensor is analyzed, and then the sensor selection principle is derived to decide which sensor channel should be attacked. Finally, two numerical simulation examples are provided to confirm the theoretical analysis results.

Data-Driven False Data-Injection Attack Design and Detection in Cyber-Physical Systems

In this article, a data-driven design scheme of undetectable false data-injection attacks against cyber-physical systems is proposed first, with the aid of the subspace identification technique. Then, the impacts of undetectable false data-injection attacks are evaluated by solving a constrained optimization problem, with the constraints of undetectability and energy limitation considered. Moreover, the detection of designed data-driven false data-injection attacks is investigated via the coding theory. Finally, the simulations on the model of a flight vehicle are illustrated to verify the effectiveness of the proposed methods.

A Secure Control Learning Framework for Cyber-Physical Systems Under Sensor and Actuator Attacks

In this article, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor and actuator attacks. Specifically, we use a bank of observer-based estimators to detect the attacks while introducing a threat-detection level function. Under nominal conditions, the system operates with a nominal-feedback controller with the developed attack monitoring process checking the reliance of the measurements. If there exists an attacker injecting attack signals to a subset of the sensors and/or actuators, then the attack mitigation process is triggered and a two-player, zero-sum differential game is formulated with the defender being the minimizer and the attacker being the maximizer. Next, we solve the underlying joint state estimation and attack mitigation problem and learn the secure control policy using a reinforcement-learning-based algorithm. Finally, two illustrative numerical examples are provided to show the efficacy of the proposed framework.

Error-Robust Distributed Denial of Service Attack Detection Based on an Average Common Feature Extraction Technique

In recent years, advanced threats against Cyber–Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To face these challenges, this paper proposes a novel error-robust multidimensional technique for DDoS attack detection. By applying the well-known Higher Order Singular Value Decomposition (HOSVD), initially, the average value of the common features among instances is filtered out from the dataset. Next, the filtered data are forwarded to machine learning classification algorithms in which traffic information is classified as a legitimate or a DDoS attack. In terms of results, the proposed scheme outperforms traditional low-rank approximation techniques, presenting an accuracy of 98.94%, detection rate of 97.70% and false alarm rate of 4.35% for a dataset corruption level of 30% with a random forest algorithm applied for classification. In addition, for error-free conditions, it is found that the proposed approach outperforms other related works, showing accuracy, detection rate and false alarm rate of 99.87%, 99.86% and 0.16%, respectively, for the gradient boosting classifier.

Optimal Stealthy Deception Attack Against Cyber-Physical Systems

This paper studies the problem of designing the optimal deception attack to maximize a utility function with the Kullback-Leibler divergence adopted as a detection constraint. The utility function reflects the goal of pulling the state away from the origin, increasing the cost of the controller and decreasing the cost of the attacker. To analyze the stealthiness of the attack, the attack signal is decomposed into two parts, one of which is strict stealthy. The necessary and sufficient condition is derived for the case that the strict stealthy attack cannot lead to an unbounded benefit. In this case, the linear transformation of the optimal attack is proved to be a Gaussian distribution. With the mean value and covariance of the Gaussian distribution as variables, the original problem is transformed into a new problem which may not be convex. A suboptimal attack policy is provided and the upper bound for the loss of benefit when using the suboptimal attack is also given. A numerical example of unmanned ground vehicle is illustrated to verify the effectiveness of the proposed attack policy.

Distributed Secure Control Against Denial-of-Service Attacks in Cyber-Physical Systems Based on K-Connected Communication Topology

In this article, the security problem in cyber-physical systems (CPSs) against denial-of-service (DoS) attacks is studied from the perspectives of the designs of communication topology and distributed controller. To resist the DoS attacks, a new construction algorithm of the k -connected communication topology is developed based on the proposed necessary and sufficient criteria of the k -connected graph. Furthermore, combined with the k -connected topology, a distributed event-triggered controller is designed to guarantee the consensus of CPSs under mode-switching DoS (MSDoS) attacks. Different from the existing distributed control schemes, a new technology, that is, the extended Laplacian matrix method, is combined to design the distributed controller independent on the knowledge and the dwell time of DoS attack modes. Finally, the simulation example illustrates the superiority and effectiveness of the proposed construction algorithm and a distributed control scheme.