metadata
license: cc-by-4.0
datasets:
- CS2CD/Context_window_256
metrics:
- accuracy
- roc_auc
- recall
- precision
- f1
tags:
- transformer
- game
- Counter-Strike2
- CS2
- counter-strike
- Cheat-detection
AntiCheatPT_256
This Model is the best performing transformer-based model from the thesis: AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games by Mille Mei Zhen Loo & Gert Luzkov.
The thesis can be found here
Results
| Metric | Value |
|---|---|
| Accuracy | 0.8917 |
| ROC AUC | 0.9336 |
| Precision | 0.8513 |
| Recall | 0.6313 |
| Specificity | 0.9678 |
| F1 | 0.7250 |
Model architecture
| Component | Value |
|---|---|
| Context window size | 256 |
| Transformer layers | 4 |
| Attention heads | 1 |
| Transformer feedforward dimension | 176 |
| Loss function | Binary Cross Entropy (BCEWithLogitLoss) |
| Optimiser | AdamW (learning rate = 10-4) |
| Scheduler | StepLR (gamma = 0.5, step size = 10) |
| Batch size | 128 |
Data
The input data used for this model was the Context_window_256 dataset based on the CS2CD dataset.
Model testing
Various validation metrics of training can be seen below:
The model confusion matrix on test data can be seen below:
Usage notes
- The dataset is formated in UTF-8 encoding.
- Researchers should cite this dataset appropriately in publications.
Application
- Cheat detection
Acknowledgements
A big heartfelt thanks to Paolo Burelli for supervising the project.