Semantic Malware Classification Using Artificial Intelligence Techniques  

作　　者：Eliel Martins Javier Bermejo Higuera Ricardo Sant’Ana Juan Ramón Bermejo Higuera Juan Antonio Sicilia Montalvo Diego Piedrahita Castillo 

机构地区：[1]Systems Development Center,Brazilian Army,QGEx,Bloco G,2°Piso-SMU,Brasilia,70630-901,DF,Brazil [2]School of Engineering and Technology,International University of La Rioja,Avda.de La Paz,137,Logrono,26006,La Rioja,Spain [3]Faculty of Technology and Science,Camilo Jose Cela University,Castillo de Alarcon 49,Villanueva de la Canada,Madrid,28692,Spain

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期3031-3067,共37页工程与科学中的计算机建模(英文)

摘　　要：The growing threat of malware,particularly in the Portable Executable(PE)format,demands more effective methods for detection and classification.Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance.This research applies deep learning to malware detection,using Convolutional Neural Network(CNN)architectures adapted to work with semantically extracted data to classify malware into malware families.Starting from the Malconv model,this study introduces modifications to adapt it to multi-classification tasks and improve its performance.It proposes a new innovative method that focuses on byte extraction from Portable Executable(PE)malware files based on their semantic location,resulting in higher accuracy in malware classification than traditional methods using full-byte sequences.This novel approach evaluates the importance of each semantic segment to improve classification accuracy.The results revealed that the header segment of PE files provides the most valuable information for malware identification,outperforming the other sections,and achieving an average classification accuracy of 99.54%.The above reaffirms the effectiveness of the semantic segmentation approach and highlights the critical role header data plays in improving malware detection and classification accuracy.

关 键 词：MALWARE portable executable SEMANTIC convolutional neural networks 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]