  Machine learning and deep learning (ML/DL) have been extensively applied in
malware detection, and some existing methods demonstrate robust performance.
However, several issues persist in the field of malware detection: (1) Existing
work often overemphasizes accuracy at the expense of practicality, rarely
considering false positive and false negative rates as important metrics. (2)
Considering the evolution of malware, the performance of classifiers
significantly declines over time, greatly reducing the practicality of malware
detectors. (3) Prior ML/DL-based efforts heavily rely on ample labeled data for
model training, largely dependent on feature engineering or domain knowledge to
build feature databases, making them vulnerable if correct labels are scarce.
With the development of computer vision, vision-based malware detection
technology has also rapidly evolved. In this paper, we propose a visual malware
general enhancement classification framework, `PromptSAM+', based on a large
visual network segmentation model, the Prompt Segment Anything Model(named
PromptSAM+). Our experimental results indicate that 'PromptSAM+' is effective
and efficient in malware detection and classification, achieving high accuracy
and low rates of false positives and negatives. The proposed method outperforms
the most advanced image-based malware detection technologies on several
datasets. 'PromptSAM+' can mitigate aging in existing image-based malware
classifiers, reducing the considerable manpower needed for labeling new malware
samples through active learning. We conducted experiments on datasets for both
Windows and Android platforms, achieving favorable outcomes. Additionally, our
ablation experiments on several datasets demonstrate that our model identifies
effective modules within the large visual network.
