Detection, estimation, and compensation of false data injection attack for UAVs

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.

Trajectory tracking control for a quadrotor helicopter in the presence of cyber attacks

In this paper, the secure trajectory tracking issue is investigated for a quadrotor helicopter system with unknown-but-bounded (UBB) noises. It is assumed that the generated reference trajectory is sent to the helicopter via wireless networks which could be vulnerable to malicious attacks. The proposed encoding-decoding approach (EDA) can significantly improve the attack detection rate (DR), which is designed as a monotone and continuous function. Moreover, it is illustrated that both the DR and the tracking error can be affected by the parameter of the EDA. In addition, the zonotopic unknown input observer (ZUIO) is designed to estimate the helicopter's states, which can be utilized to deal with UBB noises. Then, the gains of both the controller and the ZUIO can be calculated by minimizing the P-radius of the corresponding zonotopes. Finally, numerical examples are provided to demonstrate that the parameter of EDA can increase the DR and decrease the false alarm rate (FAR).

Event-triggered robust distributed output feedback model predictive control for nonlinear MASs against false data injection attacks

In this paper, an event-triggered distributed output feedback model predictive control scheme for the nonlinear disturbed multiagent systems with sensor-controller channel false data injection attacks is proposed. To provide valid system states to the controller in the event of cyber attacks, a robust multivariate observer is designed to realize the estimation and separation of uncompromised system states, false data injection attacks, and measurement disturbances, simultaneously. Based on these reconstructed signals and a newly-designed linear robustness constraint, the distributed predictive controller is established to achieve smooth cooperative stabilization among agents. Meanwhile, an event-triggered mechanism is applied to save computing resources, and it restricts the error of predictive states and estimated states to guarantee the feasibility of the optimization control problem. Theoretical analyses on robustness and security for the nonlinear multiagent systems under event-triggered distributed output feedback model predictive control are presented. Finally, a simulation on two pairs of one-link flexible joint manipulator systems verifies the theoretical results.

Security correction control of stochastic cyber-physical systems subject to false data injection attacks with heterogeneous effects

In this paper, the interconnected observer intervention-based security correction control idea is proposed for stochastic cyber-physical systems (CPSs) subjected to false data injection attacks (FDIAs). The FDIAs are injected into the controller-to-actuator channel by the adversary via wireless transmission. In particular, the FDIAs with heterogeneous effects are constructed, which consist of periodic attacks with unknown parameters and bias injection attacks with asymptotic convergence property. A novel interconnected adaptive observer structure is designed to online estimate the heterogeneous attack effects. The security correction control scheme with resilience is presented by integrating interconnected adaptive observer and robust technology. It is demonstrated that the impaired state signals can be corrected and desired security performance can be guaranteed for stochastic CPSs under FDIAs with heterogeneous effects. Finally, two simulation verifications, including a F-16 longitudinal dynamics system controlled by network, are established to verify the validity and feasibility for the presented strategy.