MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks  

作　　者：Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad 

机构地区：[1]Department of Computer Engineering,College of Computer and Information Sciences,King Saud University,Riyadh,11362,Saudi Arabia [2]Centre of Artificial Intelligence,Naif Arab University for Security Sciences,Riyadh,14812,Saudi Arabia [3]School of Arts and Sciences,The University of Notre Dame,Sydney,NSW 2007,Australia [4]Center for Smart Analytics,Institute of Innovation,Science and Sustainability,Federation University Australia,Berwick,VIC 3806,Australia [5]Department of Information Technology,Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期1009-1027,共19页工程与科学中的计算机建模(英文)

基　　金：funded by Naif Arab University for Security Sciences under grant No.NAUSS-23-R11.

摘　　要：In the current digital era,new technologies are becoming an essential part of our lives.Consequently,the number ofmalicious software ormalware attacks is rapidly growing.There is no doubt,themajority ofmalware attacks can be detected by most antivirus programs.However,such types of antivirus programs are one step behind malicious software.Due to these dilemmas,deep learning become popular in the detection and classification of malicious data.Therefore,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models.In this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and spyware.We applied the Principal Component Analysis(PCA)technique for feature extraction for binary classification.We used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class classification.Our proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named CIC-MalMem-2022.Theresults indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关 键 词：Attention-based CNN malware detection machine learning deep learning classification 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]