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. Subsequently, we show how the proposed metrics trigger
unique signatures under various attacks which aids in attack
reconstruction and also limit the impact of persistent attacks.
Unlike metrics such as CUSUM or EWMA, the stability of the
proposed metrics under normal conditions allows successful real
time detection of various stealthy attacks with ultra-low false
alarms.
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Attack Context Embedded Data Driven Trust Diagnostics in Smart Metering Infrastructure
Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known as attack context . Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains.
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- NSF-PAR ID:
- 10296895
- Date Published:
- Journal Name:
- ACM Transactions on Privacy and Security
- Volume:
- 24
- Issue:
- 2
- ISSN:
- 2471-2566
- Page Range / eLocation ID:
- 1 to 36
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3426739
