Distributed Kalman filtering techniques enable agents of a multiagent network to enhance
their ability to track a system and learn from local cooperation with neighbors. Enabling this …

Herein, design of false data injection attack on a distributed cyber-physical system is
considered. A stochastic process with linear dynamics and Gaussian noise is measured by …

This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) to protect the
private information of individual network agents from being acquired by honest-but-curious …

This paper deals with multi-object fusion in the presence of misbehaving sensor nodes, due
to faults or adversarial attacks. In this setting, the main challenge is to identify and then …

This article proposes a distributed Kalman filter (DKF) with enhanced robustness against
Byzantine adversaries. A Byzantine agent is a legitimate network agent that, unlike an …

This paper proposes a Byzantine-resilient consensus-based distributed filter (BR-CDF)
wherein network agents employ partial sharing of state parameters. We characterize the …

This paper presents a private-partial distributed least mean square (PP-DLMS) algorithm
that offers energy efficiency while preserving privacy and is suitable for applications with …

The paper considers the problem of propagation and detection of false information in a
distributed sensor network tasked with multi-object tracking. Leveraging the framework of …

In this paper, quickest detection of false data injection attack on remote state estimation is
considered. A set of N sensors make noisy linear observations of a discrete-time linear …

This paper addresses the problem of detecting false data injection (FDI) attacks in a
distributed network without a fusion center, represented by a connected graph among …