Cyber security framework for smart home energy management systems

Smart homes provide reliable automation control over different applications and appliances that are connected through intelligent networks. The concern of energy management is improved by providing connected and one-touch services with energy systems knowledge. External and internal attacks over the...

Full description

Saved in:
Bibliographic Details
Published in:Sustainable energy technologies and assessments 2021-08, Vol.46, p.101232, Article 101232
Main Authors: Alkatheiri, Mohammed Saeed, Alqarni, Mohammed A., Chauhdary, Sajjad Hussain
Format: Article
Language:English
Subjects:
Cybersecurity
Energy distribution
Energy management system
Predictive learning
Threat detection
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Smart homes provide reliable automation control over different applications and appliances that are connected through intelligent networks. The concern of energy management is improved by providing connected and one-touch services with energy systems knowledge. External and internal attacks over the energy management systems degrade its performance by energy exploitations or increasing energy demands. This paper proposes an online security framework (OSF) for smart energy management systems (EMS). The proposed framework is responsible for monitoring EMS operations and mitigating security threats at different time intervals. The proposed OSF Monitoring aims to inform consumers of their use pattern, which uses energy Monitoring systems to store, analyze, and subsequently provide the systems to the service provider with valuable information. For this purpose, the proposed framework monitors the variations in energy distribution and consumption based on the routine factor. It then determines the variations based on the appliances/applications’ functional requirements using differential predictive learning. The OSF-based predictive learning performance monitoring is used to detect early faults, improve productivity, minimize downtime, and reduce energy costs. This helps align the distribution and consumption factors for different appliances to identify the fluctuating threats through fore-hand predictions. The entire process is administered using a centralized cybersecurity system coupled with the EMS. The proposed framework’s performance is verified using the metrics energy efficiency, distribution interruption, failures, and detection rate.
ISSN:2213-1388
DOI:10.1016/j.seta.2021.101232