Security Against False Data-Injection Attack in Cyber-Physical Systems

In this paper, secure, remote estimation of a linear Gaussian process via observations at multiple sensors is considered. Such a framework is relevant to many cyber-physical systems and Internet-of-things applications. Sensors make sequential measurements that are shared with a fusion center; the fusion center applies a filtering algorithm to make its estimates. The challenge is the presence of a few unknown malicious sensors which can inject anomalous observations to skew the estimates at the fusion center. The set of malicious sensors may be time varying. The problems of malicious sensor detection and secure estimation are considered. First, an algorithm for secure estimation is proposed. The proposed estimation scheme uses a novel filtering and learning algorithm, where an optimal filter is learned over time by using the sensor observations in order to filter out malicious sensor observations while retaining other sensor measurements. Next, a novel detector to detect injection attacks on an unknown sensor subset is developed. Numerical results demonstrate up to 3-dB gain in the mean-squared error and up to $\text{75} \%$ higher attack detection probability under a small false alarm rate constraint, against a competing algorithm that requires additional side information.