Boadu Nkrumah1,*, Michal Asante1, Gaddafi Adbdul-Salam1, Wofa K. Adu-Gyamfi2
Journal of Cyber Security, Vol.6, pp. 131-153, 2024, DOI:10.32604/jcs.2024.053954
- 17 December 2024
Abstract The changing nature of malware poses a cybersecurity threat, resulting in significant financial losses each year. However, traditional antivirus tools for detecting malware based on signatures are ineffective against disguised variations as they have low levels of accuracy. This study introduces Data Efficient Image Transformer-Malware Classifier (DeiT-MC), a system for classifying malware that utilizes Data-Efficient Image Transformers. DeiT-MC treats malware samples as visual data and integrates a newly developed Hybrid GridBay Optimizer (HGBO) for hyperparameter optimization and better model performance under varying malware scenarios. With HGBO, DeiT-MC outperforms the state-of-the-art techniques with a strong accuracy More >
Login
Register
