Error Propagation after Reordering Attacks on Hierarchical State Estimation. / Gul, Ammara; Wolthusen, Stephen.

Proceedings of the Twelfth IFIP WG 11.10 International Conference on Critical Infrastructure Protection. ed. / Jason Staggs; Sujeet Shenoi. Springer-Verlag, 2018. p. 67-79 (IFIP Advances in Information and Communication Technology; Vol. 542).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

E-pub ahead of print

Documents

Abstract

State estimation is vital to the stability of control systems, especially in power systems, which rely heavily on measurement devices installed throughout wide-area power networks. Several researchers have analyzed the problems arising from bad data injection and topology errors, and have proposed protection and mitigation schemes. This chapter employs hierarchical state estimation based on the common weighted-least-squares formulation to study the propagation of faults in intermediate and top-level state estimates as a result of measurement reordering attacks on a single region in the bottom level. Although power grids are equipped with modern defense mechanisms such as those recommended by the ISO/IEC 62351 standard, reordering attacks are still possible. This chapter concentrates on how an inexpensive data swapping attack in one region in the lower level can influence the accuracy of other regions in the same level and upper levels, and force the system towards undesirable states. The results are validated using the IEEE 118-bus test case.
Original languageEnglish
Title of host publicationProceedings of the Twelfth IFIP WG 11.10 International Conference on Critical Infrastructure Protection
EditorsJason Staggs, Sujeet Shenoi
PublisherSpringer-Verlag
Pages67-79
Number of pages13
ISBN (Electronic)978-3-030-04537-1
ISBN (Print)978-3-030-04536-4
DOIs
Publication statusE-pub ahead of print - 18 Dec 2018

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume542
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 30326196