Jamming aided Generalized Data Attacks: Exposing Vulnerabilities in Secure Estimation
This work addresses vulnerabilities in secure estimation for power grid systems, presenting an incremental advancement by combining jamming with data injection attacks.
The paper tackles the problem of enhancing data injection attacks in power grid state estimation by incorporating jamming, showing that this generalized attack regime expands the scope of existing attacks and reduces attack costs while increasing resilience to secure measurements, with simulations on IEEE test cases demonstrating performance.
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.