IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification
Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficu...
Saved in:
Main Authors: | Dandan Zhang, Yafei Song, Qian Xiang, Yang Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-01-01
|
Series: | Alexandria Engineering Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824012109 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Method of anti-confusion texture feature descriptor for malware images
by: Yashu LIU, et al.
Published: (2018-11-01) -
Advanced Malware Detection: Integrating Convolutional Neural Networks with LSTM RNNs for Enhanced Security
by: Balsam Ridha Habeeb Alsaedi
Published: (2024-12-01) -
HoneyBow:an automated malware collection tool based on the high-interaction honeypot principle
by: ZHUGE Jian-wei1, et al.
Published: (2007-01-01) -
Review of malware detection and classification visualization techniques
by: Jinwei WANG, et al.
Published: (2023-10-01) -
Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
by: Yadigar Imamverdiyev, et al.
Published: (2025-01-01)