Malware Classification Using XGBoost and Genetic Algorithm for Hyperparameter Tuning

All human activities are being moved into the virtual world due to technological advancements. Since so much of our data is stored on computers and networks, the frequency of cyberattacks has sharply increased. Understanding the many types of malware, their danger level, defense strategies, and potential methods of infecting computers and other devices requires the ability to identify and classify them. In this research, we propose a malware categorization model. Our proposed model is based on XGBoost and uses a Genetic Algorithm for hyperparameter tuning. The system achieved high accuracy with the help of two different malware datasets used for testing and training: Malevis and Malimg.