Supervised Model: Attention Temporal Graph Convolutional Networks.
- 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 |
| 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 |