Attention Temporal Graph Convolutional Network for Cyber Physical Attacks Detection
Supervised Model: Attention Temporal Graph Convolutional Networks.
1. Attacks Detection Scheme
- tensorflow == 1.14 (conda install -c conda-forge tensorflow=1.14)
- python == 3.7
- scipy (conda install -c anaconda scipy)
- numpy (conda install -c anaconda numpy)
- matplotlib (conda install -c conda-forge matplotlib)
- pandas (conda install -c anaconda pandas)
- math
- sklearn (conda install -c anaconda scikit-learn)
Author |
Number of Attacks Detected |
S |
S_TTD |
S_CM |
TPR |
TNR |
Housh and Ohar |
7 |
0.97 |
0.965 |
0.975 |
0.953 |
0.997 |
Abokifa et al |
7 |
0.949 |
0.958 |
0.944 |
0.921 |
0.959 |
HCAE |
7 |
0.933 |
0.947 |
0.918 |
0.865 |
0.972 |
Tsiami et al |
7 |
0.931 |
0.934 |
0.928 |
0.885 |
0.971 |
Giacomoni et al |
7 |
0.927 |
0.936 |
0.917 |
0.838 |
0.997 |
Brentan et al |
6 |
0.894 |
0.857 |
0.931 |
0.889 |
0.973 |
A3T-GCN |
7 |
0.845 |
0.839 |
0.851 |
0.774 |
0.927 |
Chandy et al |
7 |
0.802 |
0.835 |
0.768 |
0.857 |
0.678 |
Pasha et al |
7 |
0.773 |
0.885 |
0.66 |
0.329 |
0.992 |
Aghashahi et al |
3 |
0.534 |
0.429 |
0.64 |
0.396 |
0.884 |
Forecasting Performance on Normal Dataset
|
Baseline Model |
Robust Mahalanobis Distance, Attention |
Minimum RMSE |
6.858166163 |
5.960369429 |
Minimum MAE |
3.3477044 |
2.7673762 |
Maximum Accuracy |
0.8372700512 |
0.8585200906 |
R2 |
-0.6772449017 |
-0.6805173159 |
Variance |
0.9530872479 |
0.9646917097 |
|
Baseline Model |
Robust Mahalanobis Distance, Attention |
Precision |
0.6355932203 |
0.7208237986 |
Recall / True Positive Rate |
0.5528255528 |
0.773955774 |
F1 Score |
0.5913272011 |
0.7464454976 |
Accuracy |
0.8496131528 |
0.8965183752 |
Specificity / True Negative |
0.9223359422 |
0.9265502709 |
Attacks Labels |
Attacks Description |
Feature Localization of A3T-GCN |
Attack 8 |
Alteration of L_T3 thresholds leading to underflow |
P_J256 = 11, L_T3 = 3 , P_J289 = 2, L_T2 = 2 |
Attack 9 |
Alteration of L_T2 |
P_J289 = 13, P_J422 = 13, P_J300 = 5, L_T7 = 2 |
Attack 10 |
Activation of PU3 |
F_PU3 = 38 , P_J280 = 28, L_T7 = 23, L_T4 = 6, P_J269 = 6, F_PU1 = 8, F_PU9 = 2 |
Attack 11 |
Activation of PU3 |
F_PU3 = 36 , P_J280 = 31, L_T7 = 23, F_PU1 = 22, L_T4 = 12, L_T6 = 11, P_J307 = 7, P_J415 = 3, F_PU6 = 2, P_J289 = 2 |
Attack 12 |
Alteration of L_T2 readings leading to overflow |
P_J289 = 7, P_J300 = 6, L_T2 = 2 |
Attack 13 |
Change the L_T7 thresholds |
L_T6 = 2 |
Attacls 17 |
Alteration of T4 signal |
L_T4 = 8 , L_T7 = 5, P_J415 = 4, L_T6 = 2 |