MalNet: A Large-Scale Image Database of Malicious Software
This work addresses the lack of available datasets for researchers and practitioners in cybersecurity, enabling broader evaluation and development of image-based malware detection techniques, though it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the problem of limited access to large-scale public datasets for image-based malware detection by releasing MalNet-Image, the largest public cybersecurity image database with over 1.2 million malware images, which is 24x larger in images and 70x larger in classes than existing databases, and they reported the first million-scale malware detection results on binary images.
Computer vision is playing an increasingly important role in automated malware detection with the rise of the image-based binary representation. These binary images are fast to generate, require no feature engineering, and are resilient to popular obfuscation methods. Significant research has been conducted in this area, however, it has been restricted to small-scale or private datasets that only a few industry labs and research teams have access to. This lack of availability hinders examination of existing work, development of new research, and dissemination of ideas. We release MalNet-Image, the largest public cybersecurity image database, offering 24x more images and 70x more classes than existing databases (available at https://mal-net.org). MalNet-Image contains over 1.2 million malware images -- across 47 types and 696 families -- democratizing image-based malware capabilities by enabling researchers and practitioners to evaluate techniques that were previously reported in propriety settings. We report the first million-scale malware detection results on binary images. MalNet-Image unlocks new and unique opportunities to advance the frontiers of machine learning, enabling new research directions into vision-based cyber defenses, multi-class imbalanced classification, and interpretable security.