Anomaly Detection in Smart Grid Networks Using Power Consumption Data

Hasina Rahman, Priyadarsi Nanda, Manoranjan Mohanty, Nazim Sheikh

2023

Abstract

Smart meters, intelligent devices used for managing energy consumption of consumers, are one of the integral components of the smart grid infrastructure. The smart metering infrastructure can facilitate a two-way communications through the Internet to leverage home energy management and remote meter reading by the service providers. As a consequence, the smart meters are extremely susceptible to various potential security threats, such as data tampering, distributed denial of services (DDoS) attack and spoofing attacks. In this paper, we put forward a scheme to detect anomalies in energy consumption data using real-world datasets. Thereby, addressing data tampering attacks. We have adapted an unsupervised machine learning method to distinguish the anomalous behaviour from the normal behaviour in energy consumption patterns of consumers. In addition, we have proposed a robust threshold mechanism for detecting abnormalities against noise, which has not been used in smart grids before. Our proposed model shows an accuracy of 94.53% in detecting anomalous patterns in energy consumption data. This accuracy surpasses the existing benchmark in anomaly detection in energy consumption data using machine learning models (Huang and Xu, 2021).

Paper Citation

in Harvard Style

Rahman H., Nanda P., Mohanty M. and Sheikh N. (2023). Anomaly Detection in Smart Grid Networks Using Power Consumption Data. In Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-666-8, SciTePress, pages 830-837. DOI: 10.5220/0012137600003555

in Bibtex Style

@conference{secrypt23,
author={Hasina Rahman and Priyadarsi Nanda and Manoranjan Mohanty and Nazim Sheikh},
title={Anomaly Detection in Smart Grid Networks Using Power Consumption Data},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2023},
pages={830-837},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012137600003555},
isbn={978-989-758-666-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Anomaly Detection in Smart Grid Networks Using Power Consumption Data
SN - 978-989-758-666-8
AU - Rahman H.
AU - Nanda P.
AU - Mohanty M.
AU - Sheikh N.
PY - 2023
SP - 830
EP - 837
DO - 10.5220/0012137600003555
PB - SciTePress