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 dis...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.DeiTMC 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 rate of 94% on theMaleViS and 92% on MalNet-Image Tiny datasets.Therefore,this work presents DeiT-MC as a promising and robust solution for classifying malware families using image analysis techniques and visualization approaches.展开更多
文摘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.DeiTMC 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 rate of 94% on theMaleViS and 92% on MalNet-Image Tiny datasets.Therefore,this work presents DeiT-MC as a promising and robust solution for classifying malware families using image analysis techniques and visualization approaches.
