Jamming aided Generalized Data Attacks: Exposing Vulnerabilities in Secure Estimation

TL;DR

This paper introduces a generalized attack framework combining jamming and data injection to expose vulnerabilities in secure state estimation, using graph-based methods and simulations on IEEE test cases.

Contribution

It develops a unified graph-theoretic approach to optimize jamming and data injection attacks, expanding the scope of existing attack models and highlighting the need for securing all measurements.

Findings

Jamming reduces attack costs significantly.

Optimal attacks can be characterized by graph cuts.

Securing all measurements is necessary for defense.

Abstract

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.