  Smart grid is an emerging and promising technology. It uses the power of
information technologies to deliver intelligently the electrical power to
customers, and it allows the integration of the green technology to meet the
environmental requirements. Unfortunately, information technologies have its
inherent vulnerabilities and weaknesses that expose the smart grid to a wide
variety of security risks. The Intrusion detection system (IDS) plays an
important role in securing smart grid networks and detecting malicious
activity, yet it suffers from several limitations. Many research papers have
been published to address these issues using several algorithms and techniques.
Therefore, a detailed comparison between these algorithms is needed. This paper
presents an overview of four data mining algorithms used by IDS in Smart Grid.
An evaluation of performance of these algorithms is conducted based on several
metrics including the probability of detection, probability of false alarm,
probability of miss detection, efficiency, and processing time. Results show
that Random Forest outperforms the other three algorithms in detecting attacks
with higher probability of detection, lower probability of false alarm, lower
probability of miss detection, and higher accuracy.
