False power consumption data injected from compromised smart meters in Advanced Metering Infrastructure (AMI) of smart grids is a threat that negatively affects both customers and utilities. In particular, organized and stealthy adversaries can launch various types of data falsification attacks from multiple meters using smart or persistent strategies. In this paper, we propose a real time, two tier attack detection scheme to detect orchestrated data falsification under a sophisticated threat model in decentralized micro-grids. The first detection tier monitors whether the Harmonic to Arithmetic Mean Ratio of aggregated daily power consumption data is outside a normal range known as safe margin. To confirm whether discrepancies in the first detection tier is indeed an attack, the second detection tier monitors the sum of the residuals (difference) between the proposed ratio metric and the safe margin over a frame of multiple days. If the sum of residuals is beyond a standard limit range, the presence of a data falsification attack is confirmed. Both the ‘safe margins’ and the ‘standard limits’ are designed through a ‘system identification phase’, where the signature of proposed metrics under normal conditions are studied using real AMI micro-grid data sets from two different countries over multiple years.more »
Detection of False Data Injection Attacks in Smart Grids Based on Forecasts
The bi-directional communication capabilities that emerged into the smart power grid play a critical role in the grid's secure, reliable and efficient operation. Nevertheless, the data communication functionalities introduced to Advanced Metering Infrastructure (AMI) nodes end the grid's isolation, and expose the network into an array of cyber-security threats that jeopardize the grid's stability and availability. For instance, malware amenable to inject false data into the AMI can compromise the grid's state estimation process and lead to catastrophic power outages. In this paper, we explore several statistical spatio-temporal models for efficient diagnosis of false data injection attacks in smart grids. The proposed methods leverage the data co-linearities that naturally arise in the AMI measurements of the electric network to provide forecasts for the network's AMI observations, aiming to quickly detect the presence of “bad data”. We evaluate the proposed approaches with data tampered with stealth attacks compiled via three different attack strategies. Further, we juxtapose them against two other forecasting-aided detection methods appearing in the literature, and discuss the trade-offs of all techniques when employed on real-world power grid data, obtained from a large university campus.
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- Detection of False Data Injection Attacks in Smart Grids Based on Forecasts
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- National Science Foundation
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