Reducing Malware labeling Efforts Through Efficient Prototype Selection

Malware detection and malware family classification are of great importance to network and system security. Currently, the wide adoption of deep learning models has greatly improved the performance of those tasks. However, deep-learning-based methods greatly rely on large-scale high-quality datasets, which require manual labeling. Obtaining a large-scale high-quality labeled dataset is extremely d...