English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Utility companies are increasingly leveraging residential demand flexibilityand the proliferation of smart/IoT devices to enhance the effectiveness ofresidential demand response (DR) programs through automated device scheduling.However, the adoption of distributed architectures in these systems exposesthem to the risk of false data injection attacks (FDIAs), where adversaries canmanipulate decision-making processes by injecting false data. Given the limitedcontrol utility companies have over these distributed systems and data, theneed for reliable implementations to enhance the resilience of residential DRschemes against FDIAs is paramount. In this work, we present a comprehensiveframework that combines DR optimisation, anomaly detection, and strategies formitigating the impacts of attacks to create a resilient and automated devicescheduling system. To validate the robustness of our framework against FDIAs,we performed an evaluation using real-world data sets, highlighting itseffectiveness in securing residential DR systems.

Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks