Towards Detecting Data Integrity Attacks in Smart Grid

Published

October 6, 2017

Author(s)

Linqiang Ge, Wei Yu, Paul Moulema, Guobin Xu, David W. Griffith, Nada T. Golmie

Abstract

An effective operation of the smart grid relies on the integration of sensing, computing, and communication. Attempting to disrupt the system, an adversary may launch cyber-attacks against the smart grid by compromising components, including meters, sensors, communication networks, etc. Data integrity attacks, which can threaten system operations, have recently received increased attention. Specifically, load-altering attacks that can disrupt demand response operations of the smart grid. To avoid being detected, the adversary could manipulate the strength of the attacks (e.g., the volume of injected data) to disrupt the effectiveness of system operations. To address this issue, in this chapter, we present a framework to explore attack scenarios and to design detection schemes. In our investigation, we implement three detection schemes: statistical anomaly-based detection, machine learning-based detection, and sequential hypothesis testing-based detection schemes. We conduct performance evaluation to validate the effectiveness of our detection schemes and discussed several issues related to our study.

Citation

Security and Privacy in Cyber-Physical Systems: Foundations and Application

Publisher Info

John Wiley & Sons, Inc., Hoboken, NJ

Pub Type

Books

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DOI Link

Keywords

smart grid, communication

Citation

Ge, L. , Yu, W. , Moulema, P. , Xu, G. , Griffith, D. and Golmie, N. (2017), Towards Detecting Data Integrity Attacks in Smart Grid, John Wiley & Sons, Inc., Hoboken, NJ, [online], https://doi.org/10.1002/9781119226079.ch14 (Accessed December 7, 2024)