Paper Info
Title
Inherent Vulnerability of Demand Response Optimisation against False Data Injection Attacks in Smart Grids
Abstract
The transition of energy networks to so-called smart grids benefits from advancements in Internet of Things technology. Energy management systems enable efficient and effective demand response (DR) schemes optimising load distribution. The increased user involvements through such DR schemes creates a new vector for false data injection attacks (FDIA), where authentic users themselves inject false data. Unlike in most existing FDIAs, no breaches to communication or devices are needed to execute this type of FDIA. In this work, we depict that this new FDIA can impact any optimisation-based DR scheme. Further, we show that adversaries achieve financial benefits independently from the actual algorithm used for optimisation, as long as they are able to inject false demand predictions. Compared to traditional FDIAs, reliable security mechanisms such as proper authentication, security protocols, security controls or sealed/controlled devices cannot prevent this new type of FDIA. Additionally, we show that there is no straightforward solution and we highlight the need for highly reliable FDIA detection mechanisms to thwart this type of attacks.
Year
DOI
Venue
2020
10.1109/NOMS47738.2020.9110476
NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium
Keywords
DocType
ISSN
Demand response,False data injection attack,Inherent vulnerabilities,Smart grids
Conference
1542-1201
ISBN
Citations 
PageRank 
978-1-7281-4974-5
2
0.39
References 
Authors
20
4
Name
Order
Citations
PageRank
Thusitha Dayaratne141.43
Carsten Rudolph242.78
Ariel Liebman3102.89
Mahsa Salehi4407.54