### Abstract

Decentralized data-injection attack construction with minimum mean-square-error state estimation is studied in a game-theoretic setting. Within this framework, the interaction between the network operator and the set of attackers, as well as the interactions among the attackers, are modeled by a game in normal form. A novel utility function that captures the trade-off between the maximum distortion that an attack can introduce and the probability of the attack being detected by the network operator is proposed. Under the assumption that the state variables can be modeled as a multivariate Gaussian random process, it is shown that the resulting game is a potential game. The cardinality of the corresponding set of Nash Equilibria (NEs) of the game is analyzed. It is shown that attackers can agree on a data-injection vector construction that achieves the best trade-off between distortion and detection probability by sharing only a limited number of bits offline. Interestingly, this vector construction is also shown to be an NE of the resulting game.

Original language | English (US) |
---|---|

Title of host publication | 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016 |

Publisher | IEEE Computer Society |

Volume | 2016-August |

ISBN (Electronic) | 9781467378024 |

DOIs | |

State | Published - Aug 24 2016 |

Event | 19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain Duration: Jun 25 2016 → Jun 29 2016 |

### Other

Other | 19th IEEE Statistical Signal Processing Workshop, SSP 2016 |
---|---|

Country | Spain |

City | Palma de Mallorca |

Period | 6/25/16 → 6/29/16 |

### Fingerprint

### Keywords

- Data-injection attacks
- decentralized attacks
- game theory
- state estimation

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Applied Mathematics
- Signal Processing
- Computer Science Applications

### Cite this

*2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016*(Vol. 2016-August). [7551823] IEEE Computer Society. https://doi.org/10.1109/SSP.2016.7551823

**Decentralized MMSE attacks in electricity grids.** / Esnaola, Inaki; Perlaza, Samir M.; Poor, H. Vincent; Kosut, Oliver.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016.*vol. 2016-August, 7551823, IEEE Computer Society, 19th IEEE Statistical Signal Processing Workshop, SSP 2016, Palma de Mallorca, Spain, 6/25/16. https://doi.org/10.1109/SSP.2016.7551823

}

TY - GEN

T1 - Decentralized MMSE attacks in electricity grids

AU - Esnaola, Inaki

AU - Perlaza, Samir M.

AU - Poor, H. Vincent

AU - Kosut, Oliver

PY - 2016/8/24

Y1 - 2016/8/24

N2 - Decentralized data-injection attack construction with minimum mean-square-error state estimation is studied in a game-theoretic setting. Within this framework, the interaction between the network operator and the set of attackers, as well as the interactions among the attackers, are modeled by a game in normal form. A novel utility function that captures the trade-off between the maximum distortion that an attack can introduce and the probability of the attack being detected by the network operator is proposed. Under the assumption that the state variables can be modeled as a multivariate Gaussian random process, it is shown that the resulting game is a potential game. The cardinality of the corresponding set of Nash Equilibria (NEs) of the game is analyzed. It is shown that attackers can agree on a data-injection vector construction that achieves the best trade-off between distortion and detection probability by sharing only a limited number of bits offline. Interestingly, this vector construction is also shown to be an NE of the resulting game.

AB - Decentralized data-injection attack construction with minimum mean-square-error state estimation is studied in a game-theoretic setting. Within this framework, the interaction between the network operator and the set of attackers, as well as the interactions among the attackers, are modeled by a game in normal form. A novel utility function that captures the trade-off between the maximum distortion that an attack can introduce and the probability of the attack being detected by the network operator is proposed. Under the assumption that the state variables can be modeled as a multivariate Gaussian random process, it is shown that the resulting game is a potential game. The cardinality of the corresponding set of Nash Equilibria (NEs) of the game is analyzed. It is shown that attackers can agree on a data-injection vector construction that achieves the best trade-off between distortion and detection probability by sharing only a limited number of bits offline. Interestingly, this vector construction is also shown to be an NE of the resulting game.

KW - Data-injection attacks

KW - decentralized attacks

KW - game theory

KW - state estimation

UR - http://www.scopus.com/inward/record.url?scp=84987901632&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84987901632&partnerID=8YFLogxK

U2 - 10.1109/SSP.2016.7551823

DO - 10.1109/SSP.2016.7551823

M3 - Conference contribution

AN - SCOPUS:84987901632

VL - 2016-August

BT - 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016

PB - IEEE Computer Society

ER -