"SGID"

"In recent years, smart grid systems have been widely deployed to improve the quality of electricity service provided to residential and industrial areas. Electricity smart metering and digital transfer can improve the quality of immediate and long-term decisions made by electricity service providers. However, there are multiple security issues linked to smart grid systems. Malicious data injection into smart grid systems can compromise their operation. This work aims to create a semi-synthetic dataset that includes real normal smart meter readings and multiple forms of maliciously injected data (SGID), which is synthetically generated. The results show that different machine-learning models can detect intrusive data and distinguish it from normal data."