A Diversity Index based Scoring Framework for Identifying Smart Meters Launching Stealthy Data Falsification Attacks

A challenging problem in Advanced Metering Infrastructure (AMI) of smart grids is the identification of smart meters under the control of a stealthy adversary, that inject very low margins of stealthy data falsification. The problem is challenging due to wide legitimate variation in both individual and aggregate trends in real world power consumption data, making such stealthy attacks unrecognizable by existing approaches. In this paper, via proposed modified diversity index scoring metric, we propose a novel information-theory inspired data driven device anomaly classification framework to identify compromised meters launching low margins of stealthy data falsification attacks. Specifically, we draw a parallelism between the effects of data falsification attacks and ecological balance disruptions and identify required mathematical modifications in existing Renyi Entropy and Hill\u27s Diversity Entropy measures. These modifications such as expected self-similarity with weighted abundance shifts across various temporal scales, and diversity order are appropriately embedded in our resulting framework. The resulting diversity index score is used to classify smart meters launching additive, deductive, and alternating switching attack types with high sensitivity (as low as 100W) compared to the existing works that perform poorly at margins of false data below 400W. Our proposed theory is validated with two different real smart meter datasets from USA and Ireland. Experimental results demonstrate successful detection sensitivity from very low to high margins of false data, thus reducing undetectable strategy space of attacks in AMI for an adversary having complete knowledge of our method