Towards Privacy-Preserving Anomaly-Based Intrusion Detection in Energy Communities

TL;DR

This paper presents a deep autoencoder-based intrusion detection system for energy communities that detects cyber threats while preserving data privacy through federated learning, demonstrating promising results in simulated scenarios.

Contribution

It introduces a novel privacy-preserving anomaly detection approach using deep autoencoders and federated learning for energy communities.

Findings

High detection accuracy across multiple attack scenarios

Effective federated model for distributed intrusion detection

Potential for real-world deployment in energy systems

Abstract

Energy communities consist of decentralized energy production, storage, consumption, and distribution and are gaining traction in modern power systems. However, these communities may increase the vulnerability of the grid to cyber threats. We propose an anomaly-based intrusion detection system to enhance the security of energy communities. The system leverages deep autoencoders to detect deviations from normal operational patterns in order to identify anomalies induced by malicious activities and attacks. Operational data for training and evaluation are derived from a Simulink model of an energy community. The results show that the autoencoder-based intrusion detection system achieves good detection performance across multiple attack scenarios. We also demonstrate potential for real-world application of the system by training a federated model that enables distributed intrusion detection while preserving data privacy.