  We address the problem of attack detection and isolation for a class of
discrete-time nonlinear systems under (potentially unbounded) sensor attacks
and measurement noise. We consider the case when a subset of sensors is subject
to additive false data injection attacks. Using a bank of observers, each
observer leading to an Input-to-State Stable (ISS) estimation error, we propose
two algorithms for detecting and isolating sensor attacks. These algorithms
make use of the ISS property of the observers to check whether the trajectories
of observers are `consistent' with the attack-free trajectories of the system.
Simulations results are presented to illustrate the performance of the proposed
algorithms.
