Encryption-Aware Anomaly Detection in Power Grid Communication Networks

TL;DR

This paper explores anomaly detection techniques for encrypted power grid communication networks, emphasizing the importance of low-level analysis and machine learning to enhance smart grid cybersecurity.

Contribution

It introduces a harmonic security concept that utilizes encrypted traffic analysis and anomaly detection, addressing challenges of traditional intrusion detection methods.

Findings

Encrypted traffic analysis shows promise for anomaly detection

Machine learning methods can identify irregular patterns in encrypted data

Further research needed to improve detection accuracy

Abstract

The shift to smart grids has made electrical power systems more vulnerable to sophisticated cyber threats. To protect these systems, holistic security measures that encompass preventive, detective, and reactive components are required, even with encrypted data. However, traditional intrusion detection methods struggle with encrypted traffic, our research focuses on the low-level communication layers of encrypted power grid systems to identify irregular patterns using statistics and machine learning. Our results indicate that a harmonic security concept based on encrypted traffic and anomaly detection is promising for smart grid security; however, further research is necessary to improve detection accuracy.