A study of the relationship of malware detection mechanisms using Artificial Intelligence
Jihyeon Song, Sunoh Choi, Jung‐Tae Kim, Kyungmin Park, Cheol-Hee Park, Jonghyun Kim, Ikkyun Kim
Abstract
Implementation of malware detection using Artificial Intelligence (AI) has emerged as a significant research theme to combat evolving various types of malwares. Researchers implement various detection mechanisms using shallow and deep learning models to counter new malware, and they continue to develop these mechanisms today. However, in the field of malware detection using AI, there are difficulties in collecting data, and it is difficult to compare research content and performance with related studies. Meanwhile, the number of well-organized papers is not sufficient to understand the overall research flow of these related studies. Before starting new research, researchers need to analyze the current state of research in the malware detection field they want to study. Therefore, based on these requirements, we present a summary of the general criteria related to malware detection and a classification table for detection mechanisms. Additionally, we have organized many studies in the field of various types of malware detection so that they can be viewed at a glance. We hope that the provided survey can help new researchers quickly understand the research flow in the field of AI-based malware detection and establish the direction for future research.