Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks

By Yanpeng Guan, Xiaohua Ge. IEEE Transactions on Signal and Information Processing over Networks, 2018

343 citations recorded by the source corpus.

This paper is concerned with the problem of joint distributed attack detection and distributed secure estimation for a networked cyber-physical system under physical and cyber attacks. The system is monitored by a wireless sensor network, in which a group of sensors is spatially distributed and the sensors’ measurements are broadcast to remote estimators via a wireless network medium. A malicious adversary simultaneously launches a false data injection attack at the physical system layer to intentionally modify the system's state and jamming attacks at the cyber layer to block the wireless transmission channels between sensors and remote estimators. The sensors’ measurements can be randomly dropped with mathematical probability if the corresponding transmission channels are deliberately jammed by the adversary. Resilient attack detection estimators are delicately constructed to provide locally reliable state estimations and detect the false data injection attack. Then, criteria for analyzing the estimation performance and designing the desired estimators are derived to guarantee the solvability of the problem. Finally, the effectiveness of the proposed approach is shown through an illustrative example.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a4ca57b5f0353434bb4211303f0fc46a02aeebd1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Qing-Long Han
2. Xianming Zhang
3. Xiaohua Ge
4. Chen Peng
5. Lei Ding
6. Dong Yue
7. Derui Ding
8. Yajuan Liu

In Control and Systems Engineering

1. Qing-Long Han
2. Xianming Zhang
3. Xiaohua Ge
4. Lei Ding
5. Derui Ding
6. Chen Peng
7. Dong Yue
8. Yajuan Liu

Citations in the corpus

• Networked control systems: a survey of trends and techniques (2020, background): “now, various types of data integrity attacks have been identified in the control-theoretic literature, such as deception attacks [38], [117], [124]–[126]; false data injection attacks [127], [128]; replay attacks [20], [22], [129]; sparse sensor attacks [130].”
• Event-Triggered $H_\infty$ Load Frequency Control for Multiarea Power Systems Under Hybrid Cyber Attacks (2019, background): “The remote state estimation over multisensor networks under deception attacks is investigated in [11] and [12].”
• Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach (2022, methodology): “And many studies on methods for DC state estimation have subsequently emerged, such as DC FDIA detection by Kalman filtering [8], network theory [9], [10], and machine learning [11].”
• Distributed Event-Triggered Estimation Over Sensor Networks: A Survey (2020, methodology): “Arguably, the following two attack detection approaches have been widely employed in the context of a distributed secure estimation framework: 1) χ2-detector approach based on statistical analysis [134]– [136] and 2) fault detection and isolation approach based on residual analysis [62], [137].”
• Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks (2018, methodology): “1 Various techniques have been employed to detect dc FDIA, such as statistical methods [16], [17], Kalman filter [18], sparse optimization [19], state forecasting [20], [21], network theory [22], time-series simulation [23], and machine learning [24]–[28].”