Impact Of False Data Injection Attacks Research Articles

Sliding Mode Control Against False Data Injection Attacks in DC Microgrid Systems

In this letter, the problem of false data injection (FDI) attacks is investigated for dc microgrid systems, which involves Markovian jumping parameters and Lévy noises. Moreover, an adaptive sliding mode controller is constructed to assure the stability of such dc microgrid systems. First, the dynamical equations of dc microgrid systems are proposed, which consider Lévy noises and Markovian jumping parameters. Meanwhile, the impact of FDI attacks is taken into account in dc microgrid systems. Next, the sliding mode motion dynamic of dc microgrid systems can be attained by designing a sliding mode surface (SMS) in the form of integral. Several sufficient conditions are proposed to guarantee the asymptotic stability for dc microgrid systems. Then, a novel sliding mode control (SMC) law is put forward to drive systems to reach and maintain a given sliding mode surface. In addition, the solution of linear matrix inequalities can determine undetermined parameters in the SMC and SMS. Ultimately, a practical example is demonstrated to prove that the proposed adaptive sliding mode controller can ensure the stability of the dc microgrid system under FDI attacks.

Fuzzy adaptive secure tracking control against unknown false data injection attacks for uncertain nonlinear systems with input quantization

This paper studies the problem of fuzzy adaptive secure tracking control for nonstrict-feedback nonlinear systems with unknown false data injection (FDI) attacks and input quantization. In the nonstrict-feedback nonlinear systems, the nonlinear functions contain all state variables so that the existing control methods will lead to the algebraic-loop problem. This difficulty is solved by combining the variable separation method and the properties of fuzzy basis functions. Furthermore, the tremor problem caused by the quantized input is solved by using the nonlinear function separation technique and the impact of FDI attacks is mitigated by constructing the attack compensators. On this basis, a secure tracking control strategy is proposed, which ensures the tracking error can converge to a small neighborhood of the origin and all signals of the closed-loop systems are bounded even in the presence of unknown FDI attacks. Different from the existing resilient control results for dealing with FDI attacks, this paper proposes a secure tracking control method of nonstrict-feedback nonlinear systems and also quantifies the input signal to reduce the signal transmission burden. Finally, the simulation results verify the effectiveness of the designed control algorithm.

A switching-based Moving Target Defense against sensor attacks in control systems

Moving Target Defense (MTD) prevents adversaries from being able to predict the effect of their attacks by adding uncertainty in the state of a system during runtime. In this paper, we present an MTD algorithm that randomly changes the availability of the sensor data, so that it is difficult for adversaries to tailor stealthy attacks while, at the same time, minimizing the impact of false-data injection attacks. Using tools from the design of state estimators, namely, observers, and switched systems, we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks. We show that the proposed MTD algorithm can be designed to guarantee the stability of the closed-loop system with desired performance. In addition, we formulate an optimization problem for the design of the parameters so as to minimize the impact of the attacks. The results are illustrated in two case studies, one about a generic linear time-invariant system and another about a vehicular platooning problem.

Multi-Objective False Data Injection Attacks of Cyber–Physical Power Systems

In cyber-physical power systems (CPPSs), false data injection attack (FDIA) has drawn much attention due to its stealthiness. It is of great importance to investigate the potential behaviors of attackers to improve the cyber-security of CPPSs. However, most FDIA models are often constructed separately on the effect of attackers or the impact of attacks. Accordingly, this brief proposes a multi-objective stealthy FDIA scheme in AC grid model. The attack model is described as a multi-objective optimization problem, where minimization of contaminated measurements and maximization of the attack impact are considered as two objectives while remaining stealthy. To deal with the established attack model, a non-dominated sorting genetic algorithm II (NSGAII) is introduced as the solver. To improve the efficiency of generating attack vector, a new representation mechanism is proposed to describe locations and values of injected states. Additionally, during the evolutionary process, we propose a mutation operation to balance the sparsity and impact of FDIA. Simulation results on the IEEE 14-bus, 30-bus, and 118-bus systems demonstrate the feasibility of the NSGAII-based FDIA model.

Decentralized Moving Target Defense for Microgrid Protection Against False-Data Injection Attacks

This paper proposes the Decentralized Moving Target Defense via Data Replication (DMTDR) framework, which increases the security of microgrids by adding two layers of uncertainty that limit the success of false-data injection attacks. The DMTDR framework exploits the scalability and low cost of IoT devices to replicate relevant sensory and control signals, which are randomly transmitted through independent communication channels. Then, one of the transmitted replicas is randomly selected to reach its intended destinations. As opposed to most moving target defense (MTD) approaches found in the literature, the proposed DMTDR does not require central coordination but instead it can be applied to any monitoring or control device that utilizes a communication channel. Furthermore, the proposed approach does not deteriorate the system performance in normal operation. The theoretical foundations for the optimal allocation of replicas per signal are developed, and fundamental limits of uncertainties introduced by the framework are calculated. Moreover, taking advantage of the replicated information, a decentralized attack detection strategy is introduced, which is able to detect the presence of malicious data by only using local information without requiring the computation of any estimation model. The proposed framework is demonstrated on a test microgrid, where the data replication and random transmission are implemented on a WiFi network with IoT devices. The results show that the proposed framework considerably improves: i) security by limiting the impact of false-data injection attacks; ii) detectability by enabling the detection of stealthy attacks.

Toward Optimal False Data Injection Attack against Self‐Triggered Model Predictive Controllers

AbstractThis paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC) systems with sample‐and‐hold input signals to address the potential security defects. First, the mathematical model of FDI attack against the considered STMPC system is established. Then, the difference between the states of the nominal system and the attacked system is explicitly calculated such that the impact of FDI attacks on the STMPC systems can be quantitatively analyzed. And finally, an efficient and effective algorithm to realize the desired FDI attack is proposed, and in order to maintain the flexibility of the attacker, the designed FDI attack algorithm is developed under different attacking scenarios, including attacking a single control node at each sampling time and attacking multiple control nodes each time. Finally, two simulation experiments are carried out based on a robot system and a cart–damper–spring system to verify the efficacy and optimality of the designed FDI attack strategy.

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.

Stability of Transactive Energy Market-Based Power Distribution System Under Data Integrity Attack

Future smart power distribution system with multiple active consumers and aggregators exchanging real-time electricity pricing and electricity consumption information over a communication network is prone to cyberattack. The goal of this paper is to understand the impact of attacks on pricing/load signals on the physical grid within a transactive energy market framework. First, this paper models the interaction between real-time electricity price and total energy demand in the form of a discrete time nonlinear autonomous dynamical system. Second, equilibrium electricity price and energy demand associated with this coupled dynamical system is derived and conditions for bounded input bounded output (BIBO) stability are identified. Third, a BIBO stable algorithm to design real-time electricity pricing scheme from a techno-economic perspective is developed. Finally, the impact of various levels of false data injection (FDI) attack on price of electricity, demand, and distribution system voltage is investigated. The proposed model is analyzed using simulations on the IEEE 69-bus test system and the impact of FDI attack on both electricity price/demand and distribution grid voltage is quantified. This paper shows that impact of FDI attack on electricity prices is more severe than an attack on electricity demand.