Multidevice False Data Injection Attack Models of ADS-B Multilateration Systems

Location verification is a promising approach among various ADS-B security mechanisms, which can monitor announced positions in ADS-B messages with estimated positions. Based on common assumption that the attacker is equipped with only a single device, this mechanism can estimate the position state through analysis of time measurements of messages using multilateration algorithm. In this paper, we propose the formal model of multidevice false data injection attacks in the ATC system against the location verification. Assuming that attackers equipped with multiple devices can manipulate the ADS-B messages in distributed receivers without any mutual interference, such attacker can efficiently construct attack vectors to change the results of multilateration. The feasibility of a multidevice false data injection attack is demonstrated experimentally. Compared with previous multidevice attacks, the multidevice false data injection attacks can offer lower cost and more covert attacks. The simulation results show that the proposed attack can reduce the attackers’ cost by half and achieve better time synchronization to bypass the existing anomaly detection. Finally, we discuss the real-world constraints that limit their effectiveness and the countermeasures of these attacks.

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Data-Driven and Low-Sparsity False Data Injection Attacks in Smart Grid

Security and Communication Networks ◽

10.1155/2018/8045909 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Jiwei Tian ◽

Buhong Wang ◽

Xia Li

Keyword(s):

Optimization Techniques ◽

Data Driven ◽

Second Step ◽

System Matrix ◽

False Data Injection ◽

Injection Attacks ◽

Parallel Factorization ◽

Three Stages ◽

False Data Injection Attack ◽

Attack Strategy

Recent researches on data-driven and low-sparsity data injection attacks have been presented, respectively. To combine the two main goals (data-driven and low-sparsity) of research, this paper presents a data-driven and low-sparsity false data injection attack strategy. The proposed attacking strategy (EID: Eliminate-Infer-Determine) is divided into three stages. In the first step, the intercepted data is preprocessed by sparse optimization techniques to eliminate the outliers. The recovered data is then exploited to learn about the system matrix based on the parallel factorization algorithm in the second step. In the third step, the approximated system matrix is applied for the design of sparse attack vector based on the convex optimization. The simulation results show that the EID attack strategy achieves a better performance than the improved ICA-based attack strategy in constructing perfect sparse attack vectors. What is more, data-driven implementation of the proposed strategy is also presented which ensures attack performance even without the prior information of the system.

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Distributed Control for AC Microgrids with False Data Injection Attacks and Time Delays

E3S Web of Conferences ◽

10.1051/e3sconf/202019403023 ◽

2020 ◽

Vol 194 ◽

pp. 03023

Author(s):

Rentao Lu ◽

Jie Wang

Keyword(s):

Distributed Control ◽

Control Strategy ◽

Time Delays ◽

Attack Detection ◽

Network Attack ◽

False Data Injection ◽

Injection Attacks ◽

Information Interaction ◽

False Data Injection Attack ◽

Detection And Localization

Distributed control is widely used in AC microgrids to maintain frequency and voltage stability and internal power balance. However, distributed control need realizes information interaction through communication network, which makes microgrid vulnerable to network attack. A distributed control strategy based on consensus theory is proposed in this paper to enhance the resilience of microgrid to attack. An attack detection and localization method is designed for the false data injection attack. The Artstein’s transformation is introduced to process the delay data and the performance of controller under delay can be enhanced. An isolated island AC microgrid model was built in Simulink platform for simulation to verify the performance of the controller. The simulation results verified the effectiveness of the control strategy against false data injection attack and time delay.

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Adaptive Resilient Control for Cyber-Physical Systems Against Unknown Injection Attacks in Sensor Networks☆

10.21203/rs.3.rs-204820/v1 ◽

2021 ◽

Author(s):

Yuehui Ji ◽

Qiang Gao ◽

Junjin Liu

Keyword(s):

Sensor Networks ◽

Cyber Physical Systems ◽

Lyapunov Theory ◽

State Feedback Control ◽

Resilient Control ◽

Physical Systems ◽

False Data Injection ◽

Injection Attacks ◽

State Observers ◽

False Data Injection Attack

Abstract An adaptive resilient control is concerned for a class of cyber-physical systems(CPSs) in presence of stealthy false data injection attack s in sensor networks and unknown strict-feedback nonlinear dynamics. As the sensors are attacked by ill-disposed hackers, the exactly measured state information is unavailable for state feedback control. After theory ratiocinations, the initial issue of false data injection attacks is transformed into nonlinear uncertainty dynamics and unknown control directions at the last step. At each step in the recursive backstepping control, extended state observers (ESOs) in active disturbance rejection control(ADRC) are investigated to approximate the lumped system uncertainties. Specially, the Nussbaum functions are introduced at the last step in the adaptive control. All the closed-loop signals are proved to be semi-globally uniformly ultimately bounded by Lyapunov theory. Finally, numerical simulations verify that the proposed control can afford favorable stabilization performance and counter false data injection attack.

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