Malware Classification using Transfer Learning and EfficientNetB4 on Malevis-Datasets

Abdul Hanan M. Rizwan Sana Tariq Aoun Muhammad

Jul 2026 · ICACNC 2026 Proceedings · pp. 41-46 · 0 citations

TL;DR

This study presents a convolutional neural network (CNN) architecture based on transfer learning using EfficientNetB4 for multi-class image classification that improves classification accuracy while reducing training time and overfitting compared to conventional CNN models trained from scratch.

Abstract

Malicious Software (Malware) is an abusive term defined as malicious pieces of code or program scripts that can damage information technology systems. In 2025, malware remained one of the most critical cybersecurity threats, with over 6.5 billion attacks globally. There are almost 560,000 new malware samples daily and more than 75 percent of organizations experiencing ransomware attacks annually. AI driven attacks, cloud exploitation and mobile malware has significantly increased both the scale and sophistication of cyber threats. Classification of malware is very important in terms of ensuring the security of information systems. In literature, many studies have been done to classify malware so far. This study presents a convolutional neural network (CNN) architecture based on transfer learning using EfficientNetB4 for multi-class image classification. The model leverages pretrained weights from ImageNet and integrates a custom classification head. The proposed approach improves classification accuracy while reducing training time and overfitting compared to conventional CNN models trained from scratch.

DOI Semantic Scholar

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