Robust identity tokens for intrusion detection in smart grids

Abstract

As cyber threats to critical infrastructures like power grids continue to grow, there is a pressing need for advanced intrusion detection systems that go beyond traditional certificate-based security. This paper introduces a decentralized approach that leverages robust identity tokens derived from device-specific behaviors and attributes. By employing generative adversarial networks (GANs) to simulate realistic network traffic, the system enhances its detection capabilities. Anomalies are identified using Hamming similarity metrics applied to these identity tokens, achieving a detection accuracy of 96% in a controlled testbed. These tokens serve a dual purpose: strengthening identity management and enabling effective intrusion detection in decentralized smart grid environments.