Abstract:
The evolution of power system as a smart grid (SG)
not only has enhanced the monitoring and control capabilities
of the power grid, but also raised its security concerns and
vulnerabilities. With a boom in Internet of Things (IoT), a lot
a sensors are being deployed across the grid. This has resulted
in huge amount of data available for processing and analysis.
Machine learning (ML) and deep learning (DL) algorithms are
being widely used to extract useful information from this data.
In this context, this paper presents a comprehensive literature
survey of different ML and DL techniques that have been used
in the smart grid cyber security area. The survey summarizes
different type of cyber threats which today’s SGs are prone to,
followed by various ML and DL-assisted defense strategies. The
effectiveness of the ML based methods in enhancing the cyber
security of SGs is also demonstrated with the help of a case study.
