Evaluating Electricity Theft Detectors in Smart Grid Networks

Mashima, Daisuke; Cárdenas, Álvaro A.

doi:10.1007/978-3-642-33338-5_11

Cited by 107 publications

(71 citation statements)

References 9 publications

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“…An Incremental Summarization and Pattern Characterization (ISPC) framework was introduced in [10] to incrementally characterize patterns in a data stream of electric meter reader and correlate these across time through incremental learning. Authors in [11][12] proposed a threat model and a predictive usage analytical toolkit for smart meter management applying time series analysis predictive modeling and regression analysis respectively, using data analytics on data collected by AMI in order to identify abnormal consumption trends and possible fraud to reduce electricity theft.…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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“…The work by Mashima and Cardenas [19] correlates energy theft with the creation of a set of time series representing the customer's electricity consumption in watt-hours. The goal of the attackers is to use the time series to force the utility to lower the energy bill.…”

Section: Theft Detectorsmentioning

confidence: 99%

“…A theft detector can be constructed by taking the average of the series over a number of measurements and check whether this is less than some threshold value. This threshold value being the minimum of daily averages taken over a pre-set number of days in the past [19].…”

Section: Theft Detectorsmentioning

confidence: 99%

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Smart Grid Cyber Security: An Overview of Threats and Countermeasures

López¹,

Sargolzaei²,

Santana³

et al. 2015

JEPE

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Abstract:The smart grid is the next generation of power and distribution systems. The integration of advanced network, communications, and computing techniques allows for the enhancement of efficiency and reliability. The smart grid interconnects the flow of information via the power line, intelligent metering, renewable and distributed energy systems, and a monitoring and controlling infrastructure. For all the advantages that these components come with, they remain at risk to a spectrum of physical and digital attacks. This paper will focus on digital vulnerabilities within the smart grid and how they may be exploited to form full fledged attacks on the system. A number of countermeasures and solutions from the literature will also be reported, to give an overview of the options for dealing with such problems. This paper serves as a triggering point for future research into smart grid cyber security.

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“…Number of Number of meters readings Kalogridis et al [7] N/A N/A Jawurek et al [9] 53 281, 112 Buchmann et al [3] 180 60, 480 Daisuke and Cárdenas [14] 108 1, 890, 000 Tudor et al (this paper) 19, 334 99, 355, 998 Table 2: Datasets from AMI The value was estimated based on values in [14] formly at random so that they can be modeled as a Poisson distribution.…”

Section: Datasetmentioning

confidence: 99%

Analysis of the impact of data granularity on privacy for the smart grid

Tudor

Almgren

Papatriantafilou

2013

Proceedings of the 12th ACM Workshop on Workshop on Privacy in the Electronic Society

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The upgrade of the electricity network to the "smart grid" has been intensified in the last years. The new automated devices being deployed gather large quantities of data that offer promises of a more resilient grid but also raise privacy concerns among customers and energy distributors.In this paper, we focus on the energy consumption traces that smart meters generate and especially on the risk of being able to identify individual customers given a large dataset of these traces. This is a question raised in the related literature and an important privacy research topic. We present an overview of the current research regarding privacy in the Advanced Metering Infrastructure. We make a formalization of the problem of de-anonymization by matching low-frequency and high-frequency smart metering datasets and we also build a threat model related to this problem. Finally, we investigate the characteristics of these datasets in order to make them more resilient to the de-anonymization process.Our methodology can be used by electricity companies to better understand the properties of their smart metering datasets and the conditions under which such datasets can be released to third parties.

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Evaluating Electricity Theft Detectors in Smart Grid Networks

Cited by 107 publications

References 9 publications

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Smart Grid Cyber Security: An Overview of Threats and Countermeasures

Analysis of the impact of data granularity on privacy for the smart grid

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