Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure

TL;DR

This paper presents a deep learning-based intrusion detection system tailored for advanced metering infrastructure in smart grids, demonstrating superior detection accuracy against various cyber-attacks through extensive empirical testing.

Contribution

It introduces a novel deep learning approach for intrusion detection in smart grid infrastructure and compares its performance with traditional machine learning algorithms.

Findings

Deep learning approach outperforms Naive Bayes, SVM, and Random Forest in detection accuracy.

The system effectively detects scanning, buffer overflow, and DoS attacks.

Proposed network architecture enables comprehensive security across the infrastructure.

Abstract

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.