Implementation of Intrusion Detection Methods for Distributed Photovoltaic Inverters at the Grid-Edge

Reducing the risk of cyber-attacks that affect the confidentiality, integrity, and availability of distributed Photovoltaic (PV) inverters requires the implementation of an Intrusion Detection System (IDS) at the grid-edge. Often, IDSs use signature or behavior-based analytics to identify potentially harmful anomalies. In this work, the two approaches are deployed and tested on a small, single-board computer; the computer is setup to monitor and detect malevolent traffic in-between an aggregator and a single PV inverter. The Snort, signature-based, analysis tool detected three of the five attack scenarios. The behavior-based analysis, which used an Adaptive Resonance Theory Artificial Neural Network, successfully identified four out of the five attacks. Each of the approaches ran on the single-board computer and decreased the chances of an undetected breach in the PV inverters control system.