Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

作     者：Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz 

作者机构：Department of Computer and Information SystemCleveland State UniversityOhio44115USA Department of ElectricalElectronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering DepartmentCollege of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical EngineeringCollege of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of MechanicalIndustrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模(英文))

年 卷 期：2025年第142卷第2期

页      面：1667-1695页

核心收录：

学科分类：0839[工学-网络空间安全] 08[工学] 

基　　金：University of Sargodha Najran University, NU 

主　　题：Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection 

摘      要：Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** 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 *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** 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.