Abstract
Smart grids are the cutting-edge electric power systems that make use of the latest digital communication technologies to supply end-user electricity, but with more effective control and can completely fill end user supply and demand. Advanced Metering Infrastructure (AMI), the backbone of smart grids, can be used to provide a range of power applications and services based on AMI data. The increased deployment of smart meters and AMI have attracted attackers to exploit smart grid vulnerabilities and try to take advantage of the AMI and smart meter’s weakness. One of the possible major attacks in the AMI environment is False Data Injection Attack (FDIA). FDIA will try to manipulate the user’s electric consumption by falsified the data supplied by the smart meter value in a smart grid system using additive and deductive attack methods to cause loss to both customers and utility providers. This paper will explore two possible attacks, the additive and deductive data falsification attack and illustrate the taxonomy of attack behaviors that results in additive and deductive attacks. This paper contributes to real smart meter datasets in order to come up with a financial impact to both energy provider and end-user.
Highlights
Data falsification was a term well-known for research or scientific misconduct until the dawn of the big data era
Two use cases were developed from the review to further understand these attacks and from the use case, an equation will be generated to assist in generating the falsified data when predicting the additive and deductive attacks in smart meter consumption
The objective of the predictive analysis is to support the assumption that when there is an attack happen in smart meter consumption it will change the original pattern of the end-user smart meter reading or to answers the main analytics question on is there any statistically significantly different in smart meter consumption when additive or deductive attack tampering the smart meter data reading
Summary
Data falsification was a term well-known for research or scientific misconduct until the dawn of the big data era. We will discuss further on the taxonomy of additive and deductive attacks that can inflict serious damage on smart meters These attacks enable the attackers to sabotage the user and to cause loss of revenue for the utility provider. The main objective of the additive attack is to increase the actual value of energy consumption in smart reading to increase the total bill consumption, while in the deductive attack, the false data injection in the smart meter reading reduces the total bill report to the energy utility provider. These attacks can be conducted by false data injection which is a rapidly growing problem in AMI [15]
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