Open Access

ARTICLE

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

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Tanveer Zia^2,3, Muhammad Hamza Faheem², Muhammad Imran⁴, Iftikhar Ahmad⁵

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

* Corresponding Author: Emad Ul Haq Qazi. Email:

(This article belongs to the Special Issue: Emerging Technologies in Information Security )

Computer Modeling in Engineering & Sciences 2025, 143(1), 1009-1027. https://doi.org/10.32604/cmes.2025.058352

Received 10 September 2024; Accepted 06 March 2025; Issue published 11 April 2025

Abstract

In the current digital era, new technologies are becoming an essential part of our lives. Consequently, the number of malicious software or malware attacks is rapidly growing. There is no doubt, the majority of malware 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 detection model is trained on the benchmark malware memory dataset named CIC-MalMem-2022. The results indicate that our model obtained high accuracy for binary and multi-class classification, 99.5% and 97.9%, respectively.

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Keywords

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