F-DETA: A Framework for Detecting Electricity Theft Attacks in Smart Grids

Krishna, Varun Badrinath; Lee, Kiryung; Weaver, Gabriel A.; Iyer, Ravishankar K.; Sanders, William H.

doi:10.1109/dsn.2016.44

Cited by 49 publications

(25 citation statements)

References 13 publications

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“…Smart meters are being hacked by tech-savvy malicious users to gain access and manipulate their electricity readings illegally. Although there has been a lot of research on electricity theft detection using smart meter data (fine-tuned predictive models for calculating various losses), it has been difficult to implement the same in real-time [42,45,[67][68][69][70][71][72][73][74][75]. With the implementation of smart plugs, the individual energy reading from any device can easily be compared with the smart meter data to infer whether electricity theft is taking place or not.…”

Section: Electricity Theft Detectionmentioning

confidence: 99%

Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid

Suryadevara

Biswal

2019

Energies

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In the current energy ecosystem, the need for a Hybrid Appliance Load Monitoring System (HALMS) to establish a smarter grid and energy infrastructure is undeniable. The increasing popularity of the Internet of Things (IoT) has suddenly pushed the demand for smart and connected devices. This review introduces the term smart plug as a device that uses IoT for establishing HALMS. These smart plugs are a handy solution to make the so-called ‘dumb’ devices smart. The strategy of smart plugs to enhance the energy management experience in connected spaces is presented. This study extensively highlights the current smart plug technologies and the relevant activities and limitations that need to overcome the requirements of HALMS.

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Section: Electricity Theft Detectionmentioning

confidence: 99%

Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid

Suryadevara

Biswal

2019

Energies

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“…The introduction of smart grids (SGs) as the integration of digital computing and communication technology with the power infrastructure has improved the systems' intelligence and has modernized the operation of the conventional power delivery schemes for improved reliability, flexibility, sustainability, security, resiliency, and energy efficiency [1][2][3]. Fundamental to SGs, is the implementation of advanced metering infrastructure (AMI) with SEMs as its key components, for the monitoring and control of systems' parameters [19][20][21].…”

Section: Smart Meter Data and Electricity Theftmentioning

confidence: 99%

“…Such adverse effects include electricity service disruption, damage to infrastructure, electricity thefts etc. [1,22].…”

Section: Smart Meter Data and Electricity Theftmentioning

confidence: 99%

“…are mostly computer-based and are being thought of to be integrated in planned smart cities whose operation solely depends on an intelligent grid 759 known as smart grids (SGs). The introduction of smart grids (SGs) as the integration of digital computing and communication technology with the power infrastructure has improved the systems' intelligence and has modernized the operation of the conventional power delivery schemes for improved reliability, flexibility, sustainability, security, resiliency, and energy efficiency [1][2][3] but are subjected to the risk of cyber-attacks and its related threats, especially with internet of things (IoT) taking over [4]. These attacks can change their patterns, appearance and are constantly evolving, hence, requiring a real-time solution to keep track and bring the attacks under control [5] the sooner they occur.…”

Section: Introductionmentioning

confidence: 99%

“…It occurs sporadically and inflicts huge economic losses and also threatens power systems' sustainability [2]. World bank data reveal India loses about 25% of their generated power, Brazil faces about 16% loss while China and US reportedly lose 6% and 5%, respectively [1] with all countries worldwide having several bitter experiences. Northeast group LLC reported that worldwide, $89.3 billion are lost due to electricity theft on yearly basis [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Electricity theft detection framework based on universal prediction algorithm

Otuoze

Mustafa

Sofimieari

et al. 2019

IJEECS

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<span lang="EN-GB">Electricity theft has caused huge losses over the globe and the trend of its perpetuation constantly evolve even as smart technologies such as smart meters are being deployed. Although the smart meters have come under some attacks, they provide sufficient data which can be analysed by an intelligent strategy for effective monitoring and detection of compromised situations. So many techniques have been employed but satisfactory result is yet to be obtained for a real-time detection of this electrical fraud. This work suggests a framework based on Universal Anomaly Detection (UAD) utilizing Lempel-Ziv universal compression algorithm, aimed at achieving a real-time detection in a smart grid environment. A number of the network parameters can be monitored to detect anomalies, but this framework monitors the energy <a name="_Hlk725881"></a>consumption data, rate of change of the energy consumption data, its date stamp and time signatures. To classify the data based on normal and abnormal behaviour, Lempel-Ziv algorithm is used to assign probability of occurrence to the compressed data of the monitored parameters. This framework can learn normal behaviours of smart meter data and give alerts during any detected anomaly based on deviation from this probability. A forced aggressivemeasure is also suggested in the framework as means of applying fines to fraudulent customers.</span>

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Dissemination control in dynamic data clustering for dense IIoT against false data injection attack

Pedroso

Santos

2022

Int J Network Mgmt

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The Internet of Things (IoT) has made possible the development of increasingly driven services, like industrial Industrial Internet of Things (IIoT) services, that often deal with massive amounts of data. Meantime, as IIoT networks grow, the threats are even greater, and false data injection (FDI) attacks stand out as being one of the most aggressive. The majority of current solutions to handle this attack do not take into account the data validation, especially on the data clustering service. Aiming to advance on the issue, this work introduces CONsensus Based Data FIlteriNg for IIoT (CONFINIT), an intrusion detection system for mitigating FDI attacks on the data dissemination service performing in dense IIoT networks. CONFINIT combines watchdog surveillance and collaborative consensus strategies for assertively excluding various FDI attacks. The simulations showed that CONFINIT compared with Dynamic Data-aware Firefly-based Clustering (DDFC) increased by up to 35%-40% the number of clusters without attackers in a gas pressure IIoT environment. CONFINIT achieved attack detection rates (DRs) of 99%, accuracy of 90, and F1 score of 0.81 in multiple IIoT scenarios, with only up to 3.2% and 3.6% of false negatives and positives rates, respectively. Moreover, under two variants of FDI attacks, called Churn and Sensitive attacks, CON-FINIT achieved DRs of 100%, accuracy of 99, and F1 of 0.93 with less than 2% of false positives and negatives rates.

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F-DETA: A Framework for Detecting Electricity Theft Attacks in Smart Grids

Cited by 49 publications

References 13 publications

Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid

Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid

Electricity theft detection framework based on universal prediction algorithm

Dissemination control in dynamic data clustering for dense IIoT against false data injection attack

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