  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 multiple agent nodes. Each agent node is equipped with a
sensor, and uses a Kalman consensus information filter (KCIF) to track a
discrete time global process with linear dynamics and additive Gaussian noise.
The state estimate of the global process at any sensor is computed from the
local observation history and the information received by that agent node from
its neighbors. At an unknown time, an attacker starts altering the local
observation of one agent node. In the Bayesian setting where there is a known
prior distribution of the attack beginning instant, we formulate a Bayesian
quickest change detection (QCD) problem for FDI detection in order to minimize
the mean detection delay subject to a false alarm probability constraint. While
it is well-known that the optimal Bayesian QCD rule involves checking the
Shriyaev's statistic against a threshold, we demonstrate how to compute the
Shriyaev's statistic at each node in a recursive fashion given our non-i.i.d.
observations. Next, we consider non-Bayesian QCD where the attack begins at an
arbitrary and unknown time, and the detector seeks to minimize the worst case
detection delay subject to a constraint on the mean time to false alarm and
probability of misidentification. We use the multiple hypothesis sequential
probability ratio test for attack detection and identification at each sensor.
For unknown attack strategy, we use the window-limited generalized likelihood
ratio (WL-GLR) algorithm to solve the QCD problem. Numerical results
demonstrate the performances and trade-offs of the proposed algorithms.
