Active Model Discrimination with Applications to Fraud Detection in Smart Buildings

Farshad Harirchi, Sze Yong, Emil Jacobsen, Necmiye Ozay

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


In this paper, we consider the problem of active model discrimination amongst a finite number of affine models with uncontrolled and noise inputs, each representing a different system operating mode that corresponds to a fault type or an attack strategy, or to an unobserved intent of another robot, etc. The active model discrimination problem aims to find optimal separating inputs that guarantee that the outputs of all the affine models cannot be identical over a finite horizon. This will enable a system operator to detect and uniquely identify potential faults or attacks, despite the presence of process and measurement noise. Since the resulting model discrimination problem is a nonlinear non-convex mixed-integer program, we propose to solve this in a computationally tractable manner, albeit only approximately, by proposing a sequence of restrictions that guarantee that the obtained input is separating. Finally, we apply our approach to attack detection in the area of cyber-physical systems security.

Original languageEnglish (US)
Pages (from-to)9527-9534
Number of pages8
Issue number1
StatePublished - Jul 2017


  • Fault
  • Input design
  • Model discrimination
  • Smart building
  • attack detection

ASJC Scopus subject areas

  • Control and Systems Engineering


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