An integrated detection system against false data injection attacks in the Smart Grid

Abstract

The Smart Grid is a new type of power grid that will use advanced communication network technologies to support more efficient energy transmission and distribution. The grid infrastructure was designed for reliability; but security, especially against cyber threats, is also a critical need. In particular, an adversary can inject false data to disrupt system operation. In this paper, we develop a false data detection system that integrates two techniques that are tailored to the different attack types that we consider. We adopt anomaly-based detection to detect strong attacks that feature the injection of large amounts of spurious measurement data in a very short time. We integrate the anomaly detection mechanism with a watermarking-based detection scheme that prevents more stealthy attacks that involve subtle manipulation of the measurement data. We conduct a theoretical analysis to derive the closed-form formulae for the performance metrics that allow us to investigate the effectiveness of our proposed detection techniques. Our experimental data show that our integrated detection system can accurately detect both strong and stealthy attacks.

Correlative monitoring for detection of false data injection attacks in smart grids

“…The "home-area view" we suggest aims to detect arbitrary data injection attempts at their origin, i.e., compromised residential smart meters. Our framework complements the above-mentioned work since the alert output signal generated by our methods could serve as an additional input 3 to [18], [26]- [28].…”

“…Such countermeasures for detecting false data injection appear in [18], [26]- [28]. [26] proposes an adaptive cumulative sum test combined with a multivariate hypothesis testing problem to prevent an erroneous grid-state estimate.…”

“…[27] studies a graph theoretic method for securing an optimal set of meter measurements so that state estimation is not compromised. [18] couples anomalybased methods with a data integrity check to combat stealth attacks, while [28] looks for inconsistent grid behavior using clustering techniques. Instead, we tackle the problem from a different vantage point.…”

“…False data injection attacks can mislead the grid's state estimation process when carefully crafted to avoid existing "bad data" detection techniques [13]. Numerous scenarios of adversaries who compromise meters and fabricate their readings are discussed in [15]- [18], including malware coordinating instantaneous demand drop, hacked data collector nodes programmed to send messages that reduce and then suddenly dramatically increase power demand, manipulation of electricity pricing, etc. This paper proposes a framework for tackling the problem of detecting false data injection attacks.…”

Towards an Iterated Game Model with Multiple Adversaries in Smart-World Systems

“…As a typical smart-world system, the smart cities that integrate energy, transportation and other smart-world components, potential adversaries may launch malicious attacks via controlling smart meter and sensor devices, and may manipulate critical information, including energy consumption/supply, the state of power transmission and distribution links, electricity prices, transportation routes, and so on [3,12,13,14,15,16]. As smart meters in the power grid subsystem, which is an essential component in the smart cities, are often deployed in the open environment, the power grid may suffer greater risks than the traditional power grid.…”