AI@nti-Malware: An intelligent framework for defending against malware attacks

Abstract

Distinguishing among types of malware is important to understanding how they infect computing systems, the level of threat that they pose, and means of protecting against them. This study develops an intelligent framework, AI@nti-Malware, that combines artificial intelligence learning, data imbalance, and feature evaluation mechanisms to establish a malware classification model that is effective for defending against malware attacks. The SMOTEENN algorithm is used to generate training data for a minority of categories to solve the problem of model offset and to improve the effectiveness of the model. The results of an analysis using the CTU-13 open dataset show that the intelligent framework with the machine learning algorithm XGBoost can reach an accuracy of 99.98%, while that with the deep learning backpropagation algorithm has an accuracy of 98.88%.