False data injection attacks against state estimation in wireless sensor networks

By Yilin Mo, Emanuele Garone, Alessandro Casavola, Bruno Sinopoli. IEEE Conference on Decision and Control, 2010

418 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/52012bd4deee9009ce397bb557342138b9ff0952

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. Lei Ding
5. Chen Peng
6. Derui Ding
7. Zidong Wang
8. Dong Yue

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. Zidong Wang

Citations in the corpus

• A Novel Data Fusion Algorithm to Combat False Data Injection Attacks in Networked Radar Systems (2018, background): “Under this assumption, estimation covariance matrix will converge to a steady state after a few steps, that is Pk,i = Pi , Kk,i = K i [36], [37].”
• 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].”
• Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter (2014, background): “This type of injection attack is undetectable by detectors used in the existing state-estimation security frameworks [3], [4].”
• 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].”
• State estimation under false data injection attacks: Security analysis and system protection (2018, positive): “In particular, an approximation method has been proposed in [16,17] to analyse the cybersecurity of the system by calculating the estimation error bound caused by the malicious attacks, and some insecurity conditions have been derived in [11, 15] to determine whether or not there exist malicious att”
• A Survey on Security Communication and Control for Smart Grids Under Malicious Cyber Attacks (2019, background): “Similar to [80], Kwon et al.”
• Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework (2020, background): “Studies generally categorise attacks on IoT devices into external and internal - attacks (Alromaihi et al. 2018; Mo et al. 2010).”
• Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks (2018, background): “In [12], the effect of FDI attacks on state estimation was studied over a sensor network.”