Modeling and Evaluation of False Data Injection Attacks (FDIA) in DER Inverters

Authors

Tanzim Jim HassanFollow
Akshay Ram Ramchandra
Farishta Rahman
Prakash Ranganathan, University of North DakotaFollow

Files

Description

This poster develops and evaluates false data injection attack (FDIA) models to enhance the cybersecurity of distributed energy resources (DER) solar inverters using real-time frequency data from two Fronius single-phase inverters. Fifteen datasets with unique attack patterns were developed and analyzed using machine learning models, where the most challenging anomaly (V3) had F1 scores between 0.425 and 0.76, while the most detectable (V1) achieved up to 0.895. These findings contribute to improving anomaly detection mechanisms for securing distributed energy resources (DER) and ensuring grid stability.

Publication Date

2-27-2025

Document Type

Poster

City

Grand Forks, ND

Disciplines

Cybersecurity

Comments

Presented at the 2025 UND Graduate Research Achievement Day.

Recommended Citation

Hassan, Tanzim Jim; Ramchandra, Akshay Ram; Rahman, Farishta; and Ranganathan, Prakash, "Modeling and Evaluation of False Data Injection Attacks (FDIA) in DER Inverters" (2025). Graduate Research Achievement Day Posters. 14.
https://commons.und.edu/grad-posters/14