@Article{cmc.2025.069951,
AUTHOR = {Kirubavathi Ganapathiyappan, Heba G. Mohamed, Abhishek Yadav, Guru Akshya Chinnaswamy, Ateeq Ur Rehman, Habib Hamam},
TITLE = {X-MalNet: A CNN-Based Malware Detection Model with Visual and Structural Interpretability},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {2},
PAGES = {1--18},
URL = {http://www.techscience.com/cmc/v86n2/64738},
ISSN = {1546-2226},
ABSTRACT = {The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to enhance generalization and reduce computational cost, enabling efficient training and deployment on limited hardware resources. To promote interpretability and transparency, the framework integrates Gradient-weighted Class Activation Mapping (Grad-CAM) and Deep SHapley Additive exPlanations (DeepSHAP), offering spatial and pixel-level visualizations that reveal how specific image regions influence classification outcomes. These explainability components support security analysts in validating the model’s reasoning, strengthening confidence in AI-assisted malware detection. Comprehensive experiments on the Malimg and Malevis benchmark datasets confirm the superior performance of X-MalNet, achieving classification accuracies of 99.15% and 98.72%, respectively. Further robustness evaluations using Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) adversarial attacks demonstrate the model’s resilience against perturbed inputs. In conclusion, X-MalNet emerges as a scalable, interpretable, and robust malware detection framework that effectively balances accuracy, efficiency, and explainability. Its lightweight design and adversarial stability position it as a promising solution for real-world cybersecurity deployments, advancing the development of trustworthy, automated, and transparent malware classification systems.},
DOI = {10.32604/cmc.2025.069951}
}