  In the recent years cyberattacks to smart grids are becoming more frequent
Among the many malicious activities that can be launched against smart grids
False Data Injection FDI attacks have raised significant concerns from both
academia and industry FDI attacks can affect the internal state estimation
processcritical for smart grid monitoring and controlthus being able to bypass
conventional Bad Data Detection BDD methods Hence prompt detection and precise
localization of FDI attacks is becomming of paramount importance to ensure
smart grids security and safety Several papers recently started to study and
analyze this topic from different perspectives and address existing challenges
Datadriven techniques and mathematical modelings are the major ingredients of
the proposed approaches The primary objective of this work is to provide a
systematic review and insights into FDI attacks joint detection and
localization approaches considering that other surveys mainly concentrated on
the detection aspects without detailed coverage of localization aspects For
this purpose we select and inspect more than forty major research contributions
while conducting a detailed analysis of their methodology and objectives in
relation to the FDI attacks detection and localization We provide our key
findings of the identified papers according to different criteria such as
employed FDI attacks localization techniques utilized evaluation scenarios
investigated FDI attack types application scenarios adopted methodologies and
the use of additional data Finally we discuss open issues and future research
directions
