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Smart grid is an alternative solution of the conventional power grid whichharnesses the power of the information technology to save the energy and meettoday's environment requirements. Due to the inherent vulnerabilities in theinformation technology, the smart grid is exposed to a wide variety of threatsthat could be translated into cyber-attacks. In this paper, we develop a deeplearning-based intrusion detection system to defend against cyber-attacks inthe advanced metering infrastructure network. The proposed machine learningapproach is trained and tested extensively on an empirical industrial datasetwhich is composed of several attack categories including the scanning, bufferoverflow, and denial of service attacks. Then, an experimental comparison interms of detection accuracy is conducted to evaluate the performance of theproposed approach with Naive Bayes, Support Vector Machine, and Random Forest.The obtained results suggest that the proposed approaches produce optimalresults comparing to the other algorithms. Finally, we propose a networkarchitecture to deploy the proposed anomaly-based intrusion detection systemacross the Advanced Metering Infrastructure network. In addition, we propose anetwork security architecture composed of two types of Intrusion detectionsystem types, Host and Network-based, deployed across the Advanced MeteringInfrastructure network to inspect the traffic and detect the malicious one atall the levels.

Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure