  Cyberspace occupies a large portion of people's life in the age of modern
technology, and while there are those who utilize it for good, there are also
those who do not. Malware is an application whose construction was not
motivated by a benign goal and it can harm, steal, or even alter personal
information and secure applications and software. Thus, there are numerous
techniques to avoid malware, one of which is to develop samples of malware so
that the system can be updated with the growing number of malwares, allowing it
to recognize when malwares attempt to enter. The Generative Adversarial Network
(GAN) sampling technique has been used in this study to generate new malware
samples. GANs have multiple variants, and in order to determine which variant
is optimal for a given dataset sample, their parameters must be modified. This
study employs Optuna, an autonomous hyperparameter tuning algorithm, to
determine the optimal settings for the dataset under consideration. In this
study, the architecture of the Optuna Optimized GAN (OOG) method is shown,
along with scores of 98.06%, 99.00%, 97.23%, and 98.04% for accuracy,
precision, recall and f1 score respectively. After tweaking the hyperparameters
of five supervised boosting algorithms, XGBoost, LightGBM, CatBoost, Extra
Trees Classifier, and Gradient Boosting Classifier, the methodology of this
paper additionally employs the weighted ensemble technique to acquire this
result. In addition to comparing existing efforts in this domain, the study
demonstrates how promising GAN is in comparison to other sampling techniques
such as SMOTE.
