ED4GAP: Efficient Detection for GOOSE-Based Poisoning Attacks on IEC 61850 Substations

Abstract

Devices in IEC 61850 substations use the generic object-oriented substation events (GOOSE) protocol to exchange protection-related events. Because of its lack of authentication and encryption, GOOSE is vulnerable to man-in-the-middle attacks. An adversary with access to the substation network can inject carefully crafted messages to impact the grid's availability. One of the most common such attacks, GOOSE-based poisoning, modifies the StNum and SqNum fields in the protocol data unit to take over GOOSE publications. We present ED4GAP, a network- level system for efficient detection of the poisoning attacks. We define a finite state machine model for network communication concerning the attacks. Guided by the model, ED4GAP analyzes network traffic out-of-band and detects attacks in real-time. We implement a prototype of the system and evaluate its detection accuracy. We provide a systematic approach to assessing bottlenecks, improving performance, and demonstrating that ED4GAP has low overhead and meets GOOSE's timing constraints.