How vulnerable is innovation-based remote state estimation : Fundamental limits under linear attacks

• This paper is concerned with the problem of how secure the innovation-based remote state estimation can be under linear attacks. A linear time-invariant system equipped with a smart sensor is studied. A metric based on Kullback–Leibler divergence is adopted to characterize the stealthiness of the attack. The adversary aims to maximize the state estimation error covariance while stay stealthy. The maximal performance degradations that an adversary can achieve with any linear first-order false-data injection attack under strict stealthiness for vector systems and ε-stealthiness for scalar systems are characterized. We also provide an explicit attack strategy that achieves this bound and compare this attack strategy with strategies previously proposed in the literature. Finally, some numerical examples are given to illustrate the results. 

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

Invariance

Linear time-invariant system

Time varying control systems

Attack strategies

Error covariances

Estimation errors

False data injection attacks

First order

IS innovations

Kullback Leibler divergence

Performance degradation

Remote state estimations

State estimation

Publication and Content Type

ref (subject category)

art (subject category)