Vulnerabilities in Lagrange-based DMPC in the context of cyber-security

Pablo Velarde, J.M. Maestre Torreblanca, Hideaki Ishii, Rudy Negenborn

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)


Autonomic computing requires reliable coordination between different systems. The unexpected behavior of any component may endanger the performance of the overall system. For this reason, it is necessary to prevent and detect this type of situations and to develop methods to react accordingly and to mitigate the possible consequences. In this work, we present an analysis of the vulnerability of a distributed model predictive control (DMPC) scheme in the context of cybersecurity. We consider different types of so-called insider attacks. In particular, we consider the presence of a malicious controller that broadcasts false information to manipulate costs for its own benefit. Also, we propose a mechanism to protect or, at least, relieve the consequences of the attack in a typical DMPC negotiation procedure. More specifically, a consensus approach that dismisses the extreme control actions is presented as a way to protect the distributed system from potential threats. Simulations are carried out to illustrate both the consequences of the attacks and the defense mechanisms.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Autonomic Computing (ICAC 2017)
EditorsXiaorui Wang, Christopher Stewart, Hui Lei
Place of PublicationPiscataway, NJ, USA
ISBN (Print)978-1-5386-1761-8
Publication statusPublished - 2017
EventICAC 2017: 14th International Conference on Autonomic Computing - Columbus, United States
Duration: 17 Jul 201721 Jul 2017


ConferenceICAC 2017: 14th International Conference on Autonomic Computing
Country/TerritoryUnited States


  • Computer security
  • Couplings
  • Cost function
  • Reliability
  • Process control
  • Computational modeling


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