## ⚡ Cyber-Physical Anomaly Detection for DER Systems

Welcome to the DER Security Binary Classification Challenge! This competition is focused on applying machine learning to a critical domain: ensuring the security and reliable operation of Distributed Energy Resources (DERs) within the power grid.

## Challenge 🎯

The transition to a cleaner energy grid relies heavily on integrating DERs, such as solar PV and battery storage systems. These systems are managed using standardized communication protocols, making them susceptible to cyber-attacks that can manipulate their physical behavior.

Your challenge is to build a binary classification model to detect anomalies in real-time cyber-physical data captured from a DER Simulator. The goal is to accurately classify an operational state as either "good" (normal/compliant operation) or "bad" (anomalous/compromised operation). A successful model will be key to developing autonomous, real-time security defenses for the modern power grid.

We look forward to seeing the innovative solutions you develop to safeguard our cyber-physical energy infrastructure!

## Acknowledgement

This dataset was gathered, in part, under a U.S. DOE CESER project called "Goaltender: Cloud-Based Defense and Response Tools for the DER Ecosystem," led by Sandia National Laboratories.