Biblio

A malware detection model based on semi-supervised learning is proposed in the paper. Our model includes mainly three parts: malware visualization, feature extraction, and classification. Firstly, the malware visualization converts malware into grayscale images; then the features of the images are extracted to reflect the coding patterns of malware; finally, a collaborative learning model is applied to malware detections using both labeled and unlabeled software samples. The proposed model was evaluated based on two commonly used benchmark datasets. The results demonstrated that compared with traditional methods, our model not only reduced the cost of sample labeling but also improved the detection accuracy through incorporating unlabeled samples into the collaborative learning process, thereby achieved higher classification performance.