  Smart grid is an alternative solution of the conventional power grid which
harnesses the power of the information technology to save the energy and meet
today's environment requirements. Due to the inherent vulnerabilities in the
information technology, the smart grid is exposed to a wide variety of threats
that could be translated into cyber-attacks. In this paper, we develop a deep
learning-based intrusion detection system to defend against cyber-attacks in
the advanced metering infrastructure network. The proposed machine learning
approach is trained and tested extensively on an empirical industrial dataset
which is composed of several attack categories including the scanning, buffer
overflow, and denial of service attacks. Then, an experimental comparison in
terms of detection accuracy is conducted to evaluate the performance of the
proposed approach with Naive Bayes, Support Vector Machine, and Random Forest.
The obtained results suggest that the proposed approaches produce optimal
results comparing to the other algorithms. Finally, we propose a network
architecture to deploy the proposed anomaly-based intrusion detection system
across the Advanced Metering Infrastructure network. In addition, we propose a
network security architecture composed of two types of Intrusion detection
system types, Host and Network-based, deployed across the Advanced Metering
Infrastructure network to inspect the traffic and detect the malicious one at
all the levels.
