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

Code: 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.