Aug 9, 2021 · In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy.

In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy.

Jan 13, 2023 · Overview of the VisMal framework. ZHONG ET AL.: MALWARE-ON-THE-BRAIN: ILLUMINATING MALWARE BYTE CODES WITH IMAGES FOR MALWARE CLASSIFICATION.

Sep 11, 2024 · The evaluation results indicate that VisMal can classify a malware sample within 5.2ms and have an average accuracy of 96.0%. Moreover, VisMal ...

A visualized malware classification framework called VisMal is proposed, which provides highly efficient categorization with acceptable accuracy.

Malware-on-the-Brain: Illuminating Malware Byte Codes With Images for Malware Classification. January 2022 · IEEE Transactions on Computers.

Papers ; Malware-on-the-Brain: Illuminating Malware Byte Codes with Images for Malware Classification ; Malware Images:Visualization and Automatic Classification.

Mar 8, 2022 · Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware ...

This paper proposes a malware classification method using the deep learning algorithm based on byte information that showed higher accuracy than the naive ...

The method extracts opcode sequence features from malicious code and builds a classification model based on artificial neural networks to effectively detect ...