An Efficient Malware Detection Method Using a Hybrid ResNet-Transformer Network and IGOA-Based Wrapper Feature Selection
Ali Abbas Hafeth, Abdu Ibrahim Abdullahi · Electronics · 2025
The growing sophistication of malware and other cyber threats presents significant challenges for detection and prevention in modern cybersecurity systems. In this paper an efficient and novel malware classification model using the Hybrid Resnet-Transformer Network (HRT-Net) and Improved Grasshopper Optimization Algorithm (IGOA) is proposed. Convolutional layers in the resnet50 model effectively extract local features from malware patterns, while the Transformer focuses on long-range dependencies and complex patterns by leveraging multi-head attention. The extracted local and global features are concatenated to create a rich feature representation, enabling precise malware detection. The Improved Grasshopper Optimization Algorithm with dynamic mutation coefficient and dynamic inertia motion weights is employed to select an optimal subset of features, reducing computational complexity and enhancing classification performance. Finally, the Ensemble Learning technique is used to robustly classify malware samples. Experimental evaluations on the Malimg dataset demonstrate the high efficiency of the proposed method, achieving an impressive accuracy of 99.77%, which shows greater efficiency compared to other recent studies.