A Classification System for Visualized Malware Based on Multiple Autoencoder Models

A Classification System for Visualized Malware Based on Multiple Autoencoder Models

In this paper, we propose a classification system that uses multiple autoencoder models for identifying malware images. It is crucial to accurately classify malware before we can deploy appropriate countermeasures to prevent them from spreading. Rapid malware classification is the first step in prep...

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Bibliographic Details
Main Authors: Jongkwan Lee, Jongdeog Lee
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Autoencoder
deep learning
malware classification
malware visualization
Online Access:https://ieeexplore.ieee.org/document/9584838/

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