dentification of Data Falsification in Smart Meter Consumption using an Integrated Statistical Technique

Abstract

False smart meter consumption data injected from compromised smart meters in Advanced Metering Infrastructure (AMI) is a threat that affects both utilities and consumers. With the increasing amount of smart meters' deployment, this will cause difficulties in the identification of fraud and malicious attempts. Due to the large scale of potential evidence, it is regarded as a grand challenge for forensic investigators in identifying relevant patterns of events. Furthermore, most of the existing works only deal with electricity theft from customers. Derived from these motivations, this study is focusing on the identification of data falsification in smart meter consumption and propose an integrated statistical technique combining interquartile range (IQR) and K-means. It is assumed that solving this challenge would help in structuring investigation findings, which able to aid investigator of law enforcement agencies and other stakeholders in reasoning and identifying compromised smart meters.