Attack detection and secure estimation under false data injection attack in cyber-physical systems
Arpan Chattopadhyay, Urbashi Mitra · 2018
In this paper, secure, remote estimation of a linear time-varying Gaussian process via observations at multiple sensors is considered. Such a framework is relevant to many cyberphysical systems and internet-of-things applications. Sensors make sequential measurements that are shared with a fusion center; the fusion center applies a form of optimal filtering to make its estimates. The challenge is the presence of 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, a novel detector to detect injection attack on an unknown sensor subset is developed. Next, an algorithm for secure estimation is proposed. The proposed estimation scheme uses a novel filtering and learning algorithm, where an optimal filter is learnt over time by using the sensor observations in order to filter out malicious sensor observations while retaining other sensor measurements. Numerical results demonstrate the efficacy of the proposed algorithms.