  Jamming refers to the deletion, corruption or damage of meter measurements
that prevents their further usage. This is distinct from adversarial data
injection that changes meter readings while preserving their utility in state
estimation. This paper presents a generalized attack regime that uses jamming
of secure and insecure measurements to greatly expand the scope of common
'hidden' and 'detectable' data injection attacks in literature. For 'hidden'
attacks, it is shown that with jamming, the optimal attack is given by the
minimum feasible cut in a specific weighted graph. More importantly, for
'detectable' data attacks, this paper shows that the entire range of relative
costs for adversarial jamming and data injection can be divided into three
separate regions, with distinct graph-cut based constructions for the optimal
attack. Approximate algorithms for attack design are developed and their
performances are demonstrated by simulations on IEEE test cases. Further, it is
proved that prevention of such attacks require security of all grid
measurements. This work comprehensively quantifies the dual adversarial
benefits of jamming: (a) reduced attack cost and (b) increased resilience to
secure measurements, that strengthen the potency of data attacks.
