@Article{cmes.2024.056850,
AUTHOR = {Muhammad Armghan Latif, Zohaib Mushtaq, Saifur Rahman, Saad Arif, Salim Nasar Faraj Mursal, Muhammad Irfan, Haris Aziz},
TITLE = {Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {142},
YEAR = {2025},
NUMBER = {2},
PAGES = {1667--1695},
URL = {http://www.techscience.com/CMES/v142n2/59366},
ISSN = {1526-1506},
ABSTRACT = {Ransomware attacks pose a significant threat to critical infrastructures, demanding robust detection mechanisms. This study introduces a hybrid model that combines vision transformer (ViT) and one-dimensional convolutional neural network (1DCNN) architectures to enhance ransomware detection capabilities. Addressing common challenges in ransomware detection, particularly dataset class imbalance, the synthetic minority oversampling technique (SMOTE) is employed to generate synthetic samples for minority class, thereby improving detection accuracy. The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features, resulting in comprehensive ransomware classification. Tested on the UNSW-NB15 dataset, the proposed ViT-1DCNN model achieved 98% detection accuracy with precision, recall, and F1-score metrics surpassing conventional methods. This approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity applications. The results demonstrate the model’s potential as an effective tool for proactive ransomware detection, especially in environments where evolving threats require adaptable and high-accuracy solutions.},
DOI = {10.32604/cmes.2024.056850}
}