  Future power networks will be characterized by safe and reliable
functionality against physical malfunctions and cyber attacks. This paper
proposes a unified framework and advanced monitoring procedures to detect and
identify network components malfunction or measurements corruption caused by an
omniscient adversary. We model a power system under cyber-physical attack as a
linear time-invariant descriptor system with unknown inputs. Our attack model
generalizes the prototypical stealth, (dynamic) false-data injection and replay
attacks. We characterize the fundamental limitations of both static and dynamic
procedures for attack detection and identification. Additionally, we design
provably-correct (dynamic) detection and identification procedures based on
tools from geometric control theory. Finally, we illustrate the effectiveness
of our method through a comparison with existing (static) detection algorithms,
and through a numerical study.
