ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.024
False Data Injection Attacks Using Matrix Recovery and PARAFAC in Smart Grid
Abstract— Existing researches demonstrate that state estimation result could be compromised by malicious
attacks. However, to construct the attack vectors, a usual assumption in most works is that the attacker has
perfect information regarding the topology even such information is difficult to acquire in practice. Recent
research shows that Parallel Factor Analysis (PARAFAC) can be used to drive the linear structure matrix of
the smart grid which can be used to carry out undetectable attacks. However, we found that the above
PARAFAC based blind attack strategy is only feasible in the environment with Gaussian noises. If there are
outliers (device malfunction or communication errors), the Bad Data Detector will easily detect the attack.
Hence, we propose a robust PARAFAC based blind attack strategy that one can use matrix recovery to
circumvent the outlier problem and construct stealthy attack vectors. The proposed attack strategies are tested
with IEEE 14-bus system. Simulations verify the feasibility of the proposed method.
Index Terms— false data injection; parallel factor analysis; matrix recovery; augmented lagrange multiplier
Jiwei Tian, Buhong Wang
Information and Navigation College’ Air Force Engineering University, CHINA
Cite: Jiwei Tian, Buhong Wang, "False Data Injection Attacks Using Matrix Recovery and PARAFAC in Smart Grid," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 138-143, Beijing, 25-27 June, 2017.