Smart home energy management system under false data injection attack

Sethi, Basant Kumar; Mukherjee, Debottam; Singh, Devender; Misra, Rakesh R.; Mohanty, Soumya R.

doi:10.1002/2050-7038.12411

Cited by 19 publications

(4 citation statements)

References 38 publications

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“…False Data Injection (FDI) is the most frequent type of attack. Sethi, Mukherjee, Singh, Misra, and Mohanty (2020) proved that these attacks affect electricity bills and load consumption drastically, proposing afterwards a resilient scheduling algorithm to overcome these effects. Furthermore, Dayaratne, Rudolph, Liebman, Salehi, and He (2019) showed that small fluctuations in energy demand from FDI attacks significantly increased the unit price and provided financial benefits to the attacker.…”

Section: Securitymentioning

confidence: 99%

Intelligent Energy Management Systems -- A Review

Mischos¹,

Eleanna²,

Vrakas³

2022

Preprint

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Climate change has become a major problem for humanity in the last two decades. One of the reasons that caused it, is our daily energy waste. People consume electricity in order to use home/work appliances and devices and also reach certain levels of comfort while working or being at home. However, even though the environmental impact of this behavior is not immediately observed, it leads to increased CO2 emissions coming from energy generation from power plants. Confronting such a problem efficiently will affect both the environment and our society. Monitoring energy consumption in real-time, changing energy wastage behavior of occupants and using automations with incorporated energy savings scenarios, are ways to decrease global energy footprint. In this review, we study intelligent systems for energy management in residential, commercial and educational buildings, classifying them in two major categories depending on whether they provide direct or indirect control. The article also discusses what the strengths and weaknesses are, which optimization techniques do they use and finally, provide insights about how these systems can be improved in the future.

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Section: Securitymentioning

confidence: 99%

Intelligent Energy Management Systems -- A Review

Mischos¹,

Eleanna²,

Vrakas³

2022

Preprint

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“…34,35 In the realm of intelligent algorithms, a neural network is good at solving prediction problems that have an indescribable nonlinear relationship between the input and output. 36,37 Currently, many studies use neural network technology to perform prediction. Some studies have been based on forward neural networks (FNNs), but they cannot completely solve the problem because FNNs cannot deal with the correlation information between sequences.…”

Section: Algorithm Selectionmentioning

confidence: 99%

Two‐layer optimal charging strategy for electric vehicles in old residential areas

Xia

Liao

Chen

2021

Int Trans Electr Energ Syst

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“…The authors concluded that the vulnerability of SHEMSs to pricing attacks will impede its adoption in smart homes. In similar line, Sethi et al Sethi et al (2020) proposed the injection of corrupted pricing data to disrupt scheduling and pricing operations. During this particular attack, the attacker, in perspective of a customer, plans to decrease the price of the electricity bill from being originally at $2.11 to $1.79 and the grid power import from 78.15kW to 76.99kW.…”

Section: Smart Energy Management Systemmentioning

confidence: 99%

False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey

Husnoo¹,

Anwar²,

Hosseinzadeh³

et al. 2021

Preprint

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With the proliferation of smart devices and revolutions in communications, electrical distribution systems are gradually shifting from passive, manually-operated and inflexible ones, to a massively interconnected cyber-physical smart grid to address the energy challenges of the future. However, the integration of several cutting-edge technologies has introduced several security and privacy vulnerabilities due to the large-scale complexity and resource limitations of deployments. Recent research trends have shown that False Data Injection (FDI) attacks are becoming one of the most malicious cyber threats within the entire smart grid paradigm. Therefore, this paper presents a comprehensive survey of the recent advances in FDI attacks within active distribution systems and proposes a taxonomy to classify the FDI threats with respect to smart grid targets. The related studies are contrasted and summarized in terms of the attack methodologies and implications on the electrical power distribution networks. Finally, we identify some research gaps and recommend a number of future research directions to guide and motivate prospective researchers.⋆ This document is the results of the research project funded by the Centre for

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Smart home energy management system under false data injection attack

Cited by 19 publications

References 38 publications

Intelligent Energy Management Systems -- A Review

Intelligent Energy Management Systems -- A Review

Two‐layer optimal charging strategy for electric vehicles in old residential areas

False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey

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