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Stage 2 components

1. Testbed

The testbed simulates both the primary plant and secondary plant of an electricity distribution substation, especially the physical distribution process and a small-scale of the process bus based on the IEC 61850 standard. The testbed runs on an Oracle VirtualBox with five virtual machines (VMs). One VM simulates a small-scale primary plant of a distribution substation using MATLAB/Simulink. The other four VMs represent different types of protection relays using OpenPLC, including three instantaneous overcurrent protections and one circuit breaker failure protection. Communication interfaces among each VM, such as GOOSE trip messages between IEDs and the primary plant, are written in C++ based on an open source library - libiec61850.

Sincerely thanks Thiago Alves for helping solve issues with OpenPLC_Simulink-Interface.

2. Datasets

A total of 31 datasets were collected, include:

15 benign behaviours (training datasets)

  • 1 behaviour under normal operation (when no events happen) -- label 0
  • 10 behaviours under emergency operation (when a line-to-line fault happens, related overcurrent protetcion triggered) -- label 101-110
  • 4 behaviours under emergency operation (when overcurrent protection failed; breaker failure protection triggered) -- label 111-114

8 malicious behaviours from IED1 (training datasets)

  • 2 false data injection attacks under normal operation -- label 901, 903
  • 2 message modification attacks under normal operation -- label 902, 904
  • 2 false data injection attacks under emergency operation -- label 905, 907
  • 2 message modification attacks under emergency operation -- label 906, 908

8 malicious behaviours from IED2 (testing datasets)

  • 2 false data injection attacks under normal operation -- label 901, 903
  • 2 message modification attacks under normal operation -- label 902, 904
  • 2 false data injection attacks under emergency operation -- label 905, 907
  • 2 message modification attacks under emergency operation -- label 906, 908

3. Machine learning models

A total of 5 models were created, include:

  • Support Vector Machine (SVM)
  • K-nearest neighbour (KNN)
  • Decision tree
  • BiLSTM without sliding window algorithms
  • BiLSTM with two types of sliding window algorithms

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