OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data
This work addresses malware detection for cybersecurity by improving data augmentation, though it appears incremental as it combines existing methods like GANs and Optuna with ensemble learning.
The study tackled the problem of generating synthetic malware samples for imbalanced tabular data by proposing OOG, an Optuna-optimized GAN sampling technique, achieving high performance metrics such as 98.06% accuracy and 98.04% F1 score.
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.