Pith. sign in

REVIEW 1 cited by

Security Metrics of Networked Control Systems under Sensor Attacks (extended preprint)

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1809.01808 v3 pith:PNOGPLZN submitted 2018-09-06 cs.SY cs.SY

classification cs.SY
keywords attackscontrolsystemsystemsmetricssecuritystealthydynamics
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

As more attention is paid to security in the context of control systems and as attacks occur to real control systems throughout the world, it has become clear that some of the most nefarious attacks are those that evade detection. The term stealthy has come to encompass a variety of techniques that attackers can employ to avoid being detected. In this manuscript, for a class of perturbed linear time-invariant systems, we propose two security metrics to quantify the potential impact that stealthy attacks could have on the system dynamics by tampering with sensor measurements. We provide analysis mathematical tools (in terms of linear matrix inequalities) to quantify these metrics for given system dynamics, control structure, system monitor, and set of sensors being attacked. Then, we provide synthesis tools (in terms of semidefinite programs) to redesign controllers and monitors such that the impact of stealthy attacks is minimized and the required attack-free system performance is guaranteed.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning

    eess.SY 2026-06 unverdicted novelty 6.0 of 10

    A set-membership data-driven min-max MPC approach is developed for unknown nonlinear systems, yielding recursive feasibility and stability guarantees via Lyapunov SDPs for noise-free and disturbed measurements.

Pith tools